Danny Luo

Thoughts on Nietzsche's Beyond Good and Evil

2023-01-21T00:00:00+00:00

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Self-Analysis

Nietzsche’s writings are full of doubt, and so, in the Maoist tradition, I must first doubt myself. What is the intention (or unintention) of this post? Is the desire to express thoughts on such a lofty (or so-perceived) subject fundamentally self-serving?

In my more naïve days, I used to call this “honest expression”, but what purpose is there to express anything with the world? Is it not enough to possess knowledge internally? Is it not enough to think in private, not enough to write it in private, not enough to discuss it in private with a trusted friend, but to publish it, to announce it to the world, in anticipation of what? I doubt it is benevolence that urges me to disseminate my “thoughts”, but rather a self-aggrandizing, pedantic motivation. How can cynical intellectuals simultaneously loathe the world but crave its attention?

What is it that I seek? What am I trying to prove? That I am cultured and intellectual, in a direction orthogonal to my tech persona, in the spirit of Bay Area technocrats? It has all been done before.

Equal parts of me desire and fear fame. In the modern context, fame is partially enslavement to the capricious masses. 人怕出名猪怕壮。Perhaps this will haunt me in forty years when I run for office.

I read Nietzsche partly as literature, not completely as strict academic philosophy, which suffers today as all academia does—from hyperspecialization.

Nietzsche lent me his voice: I compose the following ‘thoughts’ by digesting his ideas and then regurgitating them. It is difficult to say whether any of my ideas are truly original, they can be seen as variations upon his ‘tune’, improvisations on his ‘changes’. Even so, Nietzsche resists my attempts to quote him directly, he must not be taken out of context!

This post is imperfect and unfinished. I am embarrassed but not ashamed.

Preface

Here Nietzsche lays hints for the major themes he will explore in this book: the predominant dogmatic philosophy, the state of the European man, and the “tension of the bow” present in modern times.

On the Prejudices on Philosophers

Why truth? Why not untruth? Why black and white, truth and falsehood? Why not grey?

The philosopher is driven by instinct. He undergoes post-hoc reasoning to arrive at “self-evident ‘truths’”, “immediate certainties”, “absolute knowledge” and “thing in itself”, which are seductions of words (16). A modern analogy is the Humean elephant and the rider as described in The Righteous Mind by Jonathan Haidt.

The nihilists are those that prefer a “certain nothing over an uncertain everything” (10). In spite of all these modern ideas, modern advancements, we still yearn for God. We can modernize everything except ourselves.

What often is misunderstood about Nietzsche is his ‘will to power’. It is the most fundamental drive of a human, the evolutionary pressure of Homo Sapiens towards “procreation and nourishment”, that pressure which is responsible for everything we are today, everything we feel and think (36).

And you, my gentle intellectual, it does not serve you to seclude yourself in some ‘castle’, away from the mouth-breathing masses, to say, “I am the exception to the rule”. Perhaps so, but the rule is more interesting than the exception.

The paradox: Freedom of will means obedience of body.

The Free Spirit

In me and my contemporaries lies a will to nothing, similar to that infamous will to power. Do not confuse it with nihilism. Nowadays, it is not ideology or rationalism that drives us but rather technology, which exploits our innate circuits, meets the will to power face-to-face and then penetrates a level deeper. The zeitgeist of the young generation, that grew up with technology in their faces magnifying their every twitch, and training a timid self-awareness, is that of reclusive self-preservation. Those who capitalize on this are those who can become more self-unaware, who can possess an aware non-awareness, the modern answer to Nietzsche’s proposition of an independent free spirit.

Who are these ‘free spirits’? They are the doubters, the mutterers, the ones who doubt their freedom the most when others cherish it. They do not hide away in their ‘castles’, rather mull about in the crowds, masked, and strive to understand even the most unstomachable stupidity of the masses without any intention of pandering to them (26), or devising a ‘common good’ for them, for “[g]ood is no longer good when one’s neighbor mouths it … whatever can be common always has little value” (43).

‘Modern thinkers’, slaves to ‘modern ideas’ of goodness, are more unfree than the common man, who possesses the flexibility of human nature in the face of his own survival, the flexibility which ‘modern thinkers’ attempt to outlaw.

“Whatever is profound loves masks; what is most profound even hates image and parable”, shame arises in these ‘profound spirits’ for being understood too readily, the masks they wear shield them against the numerous misinterpretations made against them (40).

A man whose sense of shame has some profundity encounters his destinies and delicate decisions, too, on paths which few ever reach and of whose mere existence his closes intimates must not know: his mortal danger is concealed from their eyes, and so is his regained sureness of life. Such a concealed man who instinctively needs speech for silence and for burial in silence and who is inexhaustible in his evasion of communication, wants and sees to it that a mask of him roams in his place through the hearts and heads of his friends. And supposing he did not want it, he would still realize sone day that in spite of that a mask of him is there—and that this is well. Every profound spirit needs a mask: even more, around every profound spirit a mask is growing continually, owing to the constantly false, namely shallow, interpretation of every word, every step, every sign of life he gives.— (40)

“The text finally disappeared under the interpretation” (38), ah, friend—perhaps the text never existed at all.

Das Religiöse Wesen

What is religious is what acknowledges the people’s suffering, what rebels against the aristocratic apathy towards suffering.

Modern people are often made to be ashamed of their suffering, ashamed of not being ‘happy’, instead, one ought to find meaning in suffering and bear it with pride (Frankl, Man’s Search for Meaning). Religion allows its subjects to bear tremendous suffering, in particular, Christians indulge in their suffering in the image of their all-suffering Saviour!

The Christian religion, which demands “sacrifice of all freedom, all pride, all self-confidence of the spirit; at the same time, enslavement and self-mockery, self-mutilation” was a rebellion against those refined, classical tastes of the Romans and their lighthearted disregard for anything so religious, so ascetic, so cruel.

Modern men, obtuse to all Christian nomenclature, no longer feel the gruesome superlative that struck a classical taste in the paradoxical formula “god on the cross.” Never yet and nowhere has there been an equal boldness in inversion, anything as horrible, questioning, and questionable as this formula: it promised a revaluation of all the values of antiquity. (46)

To put “god on the cross” is to mutilate the all-knowing son of God for his love of man and for the sins of mankind.

The ecstasy of the Oriental slave against the lighthearted disregard of their Roman masters:

It is the Orient, deep Orient, it is the Oriental slave who revenged himself in this way on Rome and its noble and frivolous tolerance, on the Roman “catholicity” of faith. It has always been not faith but the freedom from faith, that half-stoical and smiling unconcern with the seriousness of faith, that enraged slaves in their masters—against their masters. “Enlightenment” enrages: for the slave wants the unconditional; he understands only what is tyrannical, in morals, too; he loves as he hates, without nuance, to the depths, to the point of pain, of sickness—his abundant concealed suffering is enraged against the noble taste that seems to deny suffering. (46)

Contrasting Christianity with Judaism, “the book of divine justice” puts on display the ruins of mankind and what man once was, this book stands at stark contrast with the compassionate “book of grace” (52).

To have glued this New Testament, a king of rococo of taste in every respect, to the Old Testament to make one book, as the “Bible,” as “the book par excellence”—that is perhaps the greatest audacity and “sin against the spirit” that literary Europe has on its conscience. (52)

How is it that the most powerful men prostrate themselves before the ‘holy fool’ (Dostoevsky, Demons)? Such sacrifice and self-denial in a man arouses a suspicion that he may know something that we do not, that he may possess a powerful spirituality that may withstand time and nature, that he may be the key to life and death.

Those who have looked down into “the most world-denying of all possible ways of thinking—beyond good and evil and no longer […] under the spell and delusion of morality” may have realized the opposite ideal of high spirituality and humanism, and become he “who not only come to terms and learned to get along with whatever was and is, but who want to have what was and is repeated into all eternity” (56). The cycle of religious beliefs continues, only the objects of beliefs change.

[T]he concepts “God” and “sin,” will seem no more important to us than a child’s toy and a child’s pain seem to an old man—and perhaps “the old man” will then be in need of another toy and another pain—still child enough, an eternal child! (57)

“[M]uch wisdom lies in the superficiality of men” (59), what is most meaningful may be what is unintentional. Could this be ideology?

Religion beautifies, falsifies man and his suffering, and turns him “into so much art, surface, play of colors, graciousness that his sight no longer makes one suffer” (59). To love man for God’s sake and not for his own sake is a noble feeling most remote from humanism. Love of man needs an ulterior, transcendental motive to make it sacred and untouchable through the turbulence of mankind. (60)

It is the profound, suspicious fear of an incurable pessimism that forces whole millennia to bury their teeth in and cling to a religious interpretation of existence: the fear of that instinct which sense that one might get a hold of the truth too soon, before man has become strong enough, hard enough, artist enough. (59)

Piety requires an aristocratic leisure, whereas modern industriousness dissolves certain religious instincts, or replaces the Catholic lavish and grace with a self-sufficient Protestant work ethic.

Religion stabilizes the social strata and teaches them how to behave, act, and accept their fate. If Christ suffered so much for all of humanity, then surely I may endure the brief suffering of my life.

Christianity turned the European into its modern form: a self-destructive herd animal that hates the earth and worldly things almost as much as it hates itself. In The Prince, Machiavelli blamed the Catholic Church and its values for the suppression of virtú and ambition (and an Italian identity), turning men into troughs of pigs.

Men, not high and hard enough to have any right to try to form man as artists; men, not strong and farsighted enough to let the foreground law of thousandfold failure and ruin prevail, thought it cost them sublime self-conquest; men, not noble enough to see the abysmally different order of rank, chasm of rank, between man and man—such men have so far held sway over the fate of Europe, with their “equal before God,” until finally a smaller, almost ridiculous type, a herd animal, something eager to please, sickly, and mediocre has been bred, the European of today— (62)

Religion and religious morality are the foundation of stable societies (Durkheim), yet sovereign, organized religions, like Roman Catholicism, have held Europe back for many centuries and preserved that which ought to have perished. Even if we view religion as a means to an end, different people across different generations have different ends, and all that remains is the means.

Epigrams and Interludes

One of few places appropriate to quote Nietzsche verbatim.

Whoever despises himself still respects himself as one who despises. (78)
It is terrible to die of thirst in the ocean. Do you have to salt your truth so heavily that it does not even—quench thirst anymore? (81)
- Such refined truth cannot give us a reason to live.
What? A great man? I always see only the actor of his own ideal. (97)
- A great man — a philosopher — creates his own values.
The voice of disappointment: “I listened for an echo and heard nothing but praise—” (99)
- The common man: “He praises me: hence he think I am right” (283).
There are no moral phenomena at all, but only a moral interpretation of phenomena— (108)
- If moral phenomena were to exist, what queer things would they be? (Mackie, Ethics: inventing right and wrong)
The will to overcome an affect [affekt] is ultimately only the will of another, or of several other, affects. (117)
- Mindfulness 101
Enjoying praise is in some people merely a courtesy of the heart—and just the opposite of vanity of the spirit. (122)
When we have to change our mind about a person, we hold the inconvenience he causes us very much against him. (125)
A people is a detour of nature to get to six or seven great men.— Yes, and then to get around them. (126)
All credibility, all good conscience, all evidence of truth come only from the senses. (134)
Whoever fights monsters should see to it that in the process he does not become a monster. And when you look long into an abyss, the abyss also looks into you. (146)
What a time experiences a evil is usually an untimely echo of what was formerly experiences as good—the atavism of a more ancient ideal. (149)
“Where the tree of knowledge stands, there is always Paradise”: thus speak the oldest and the youngest serpents. (152)
- We the banished must beware of new serpents whispering the new utopia of knowledge.
One has to repay good and ill—but why precisely to the person who has done us good or ill? (159)
In the end one loves one’s desire and not what is desired. (175)
- Philosophy offers great love advice.
The familiarity of those who are superior embitters because it may not be returned.— (182)
- It’s difficult to be friends with anyone who can fire you.
I don’t like him.”— Why?— “I am not equal to him.”— Has any human being ever answered that way? (185)

Natural History of Morals

Moral ‘scientists’ attempted to theorize a rational foundational for morality, as if it were a natural phenomenon like gravity. What they produce in the end is more akin to a pseudo-rationalized faith in their particular morality. They consider problems of abstract morality, ‘morality in a vacuum’ (but really in their own moral system), and never any problems of morality that arose in the comparison of different moralities. They accept their particular system values without any serious doubt while renouncing what they call ‘faith’, that is, a more explicit ecclesiastical belief. The moral philosophers of the past and well as the New Atheists of the present both fail to recognize that behind their scientific method lies a deep faith. (186)

That for thousands of years European thinkers thought merely in order to prove something—today, conversely, we suspect every thinker who “wants to prove something”—that the conclusions that ought to be the result of their most rigorous reflection were always settled from the start … (188)

The tyrannical moralities of the past are perhaps more natural, more human, more conducive to art and beauty, than the new and vacuous laisser aller liberalism. Even in our descent to the freest state, we are bound by multitudinous latent laws with no closed-form expression; it is precisely these laws that dictate and define us in our highest, most subconcious states. Our identities come from our restrictions, and not what exists when we ‘let go’.

[A]ll there is or has been on earth of freedom, subtlety, boldness, dance, and masterly sureness, whether in thought itself or in government, or in rhetoric and persuasion, in the arts just as in ethics, has developed only owing to the “tyranny of such capricious laws”; and in all seriousness, the probability is by no means small that precisely this is “nature” and “natural”—and not that laisser aller (188)

Morality limits freedom, demands obedience, teaches slavery of the mind, narrows the perspective, inspires fear, and ultimately teaches us to hate that laisser aller (188).

The essence of rational utopianism: No one really wants to do bad things, they only do so out of ignorance. If they knew better, they would cease to do bad. (190) Similar to Nietzsche, Dostoevsky, through the voice of the ‘Underground Man’, also regards rational utopianism as an illusory “crystal edifice” and states that man will act irrationally just to prove to himself that he is autonomous and not a “piano key” (Dostoevsky, Notes from Underground).

Why must reason and instinct tend towards the Good? Even Socrates, through self-examination, realized that he, like the Athenians who condemned him, was also a man of instinct, and that “one must follow the instincts but persuade reason to assist them with good reasons” (191). In the end, one’s reason can only suggests gently to one’s instinct. We follow our senses more than we are aware of, we detest what is foreign to us, and even our interpretations of great works are often reaffirmations of what we already knew and believed. Reason is not the authority of humanity, as Descartes believed, but a mere “instrument” (191).

What is it to possess? It is not enough for her to give herself to me, to give up all she possesses, but to give it to the real me, in all devilry, and not some mask, no matter how profound it is (194). The paradox of those profound masked individuals when lain bare before their dearest ones is the equivocation between the subtle supplication of “Understand me!” and the defiant rebuttal “You will never understand me.”

Morality as Herd Timidity

Moralists and psychologists seek a ‘pathology’ in the monsters of men where instead, there is simply nature. The corresponding modern phenomenon is the over-medicalization of perceived aberrations: Everything is a disorder, everything can be diagnosed, everything can be medicalized.

Morality is a restraint of individual dangerousness, no more meaningful than “prudence, mixed with stupidity” (198). Formerly, rulers feign obedience to transcendental values, such as the divine right of kings. Now, obedience to great rulers has been replaced by parliament and procedure, hence further transforming the European into a “herd-animal”. The abstract authority causes the people to yearn for the appearance of an unconditional commander, such as Napoleon, who strikes the “herd-animal Europeans as an immense comfort and salvation from a gradually intolerable pressure” (199).

Nietzsche’s claim that multiple values and moralities in an individual weakens him is similar to the claim by the character Shatov in Demons that when moralities become common, nations and their people become weak (Dostoevsky, Demons). Whatever values become common amongst disparate groups of people due to the mixing of races in contemporary Europe can only be the lowest common denominator. (200)

When morality is the utility of the herd, and immorality is what endangers the herd, it is “fear of the neighbor” , not “love of the neighbor” that drives moral judgment (201).

The highest and strongest drives, when they break out passionately and drive the individual far above the average and the flats of the herd conscience, wreck the self-confidence of the community, its faith in itself, and it is as if its spine snapped. Hence just these drives are branded and slandered most. High and independent spirituality, the will to stand alone, even a powerful reason are experienced as dangers; everything that elevates an individual above the herd and intimidates the neighbor is henceforth called evil and the fair, modest, submissive, conforming mentality, the mediocrity of desires attains moral designations and honors. (201)

Herd morality seeks to eradicate danger. Observe the tenderness of those who envelope even criminals in compassion: to them, punishing criminals is unpleasant, unfair—they would rather neuter than punish: “Is it not enough to render him undangerous? Why still punish? Punishing itself is terrible … Supposing that one could altogether abolish danger, the reason for fear, this morality would be abolished, too, eo ipso” (201).

[T]he imperative of herd timidity: “ we want that some day there should be nothing any more to be afraid of!” Some day—throughout Europe, the will and way to this day is now called “progress.” (201)

The evolutionary purpose of morality is herd preservation, or rather, it is only because it served this purpose—through enhanced group cooperation and increased survival rates—that it exists today. The democratic movement, the heir of the Christian movement (202), is no exception, just in modern, secular times, and with less pomp and circumstance (or perhaps more?—more reasoning, more ‘logic’ to convince someone of what was already accepted).

The man of modern ideas “now knows in Europe what Socrates thought he did not know and what that famous old serpent once promised to teach—today once ‘knows’ what is good and evil” (202).

The anarchists, nihilists, and other howling revolutionary dogs, impatient and impetuous, seem opposite to peaceful democrats and tame socialists, but are really of the same lot. The peaceful democratic movement gives rise to the later, more violent offspring, just as the 1840s gave rise to the 1860s, and 1919 gave rise to 1949. They are hostile to all forms of society other than that of the autonomous herd, they resist any means of distinguishing individuals from the masses; they oppose all privileges and special rights, eventually, they will deny all rights, “for once all are equal nobody needs any ‘rights’ any more” (202).

The culmination of the Enlightenment and all Christian and European progress is a universal pity with all that suffers—a “pity with God”. They regard this new shared pity as the height of man and regard the herd—themselves—as the saviours of mankind. (202)

The over-all degeneration of man down to what today appears to the socialist dolts and flatheads as their “man of the future”—as their ideal—this degeneration and diminution of man into the perfect herd animal (or, as they say, to the man of “free society”), this animalization of man into the dwarf animal of equal rights and claims, is possible, there is no doubt of it. (203)

Nietzsche calls to new philosophers, to ‘free spirits’ who can penetrate the blind confidence in modern ideas with independence and individual hardness. In this respect, similar to Plato and his rule by philosophers, Nietzsche is quite the idealist, even optimistic regarding what “might yet be made of man … how man is still unexhausted for the greatest possibilities”, despite the many times great men have been sunk during their becoming. (203)

The celebration of uniqueness today is yet another smoke-screen. It is a uniqueness without hierarchy, for if we are all unique, none of us are unique, there must be something to be unique against, that is, the common.

We Scholars

In this section, Nietzsche begins a dismissal of scholars, analytic thinkers, empiricists, scientists, “nimble smarties” and mechanics, all those who declared their independence from philosophy. More importantly, he raises the philosopher above the aforementioned, to a level of creative power and authority that is gained through his vast breadth of knowledge and experience (213). To Nietzsche, philosophers must have a broad look and not be detained in any specialization, especially in today’s ultra-specialized world. As fields of study become more specialized and pragmatic, philosophy has continued to be marginalized and abandoned in favour of science, and science now is beginning to be overshadowed by an even more pragmatic field. Science has become the luxury that philosophy once was!

Philosophy is not ‘wisdom’, which is the religious elevation to handle life and its wickedness. A genuine philosopher is not a secluded, wise hermit, but rather “lives ‘unphilosophically’ and ‘unwisely,’ above all imprudently, and feels the burden and duty of a hundred attempts and temptations of life—he risks himself constantly, he plays the wicked game—”(205). Old wisdom is comforting, but the more philosophy one reads, the more uncomfortable one becomes with the world.

Nietzsche dismisses the ‘scientific man’ as a sort of tinkering, industrious worker, with no capacity for authority or greatness. He is ‘mediocre’, and seeks to instill mediocrity in all.

The worst and most dangerous thing of which scholars are capable comes from their sense of the mediocrity of their own type—from that Jesuitism of mediocrity which instinctively works at the annihilation of the uncommon man and tries to break every bent bow, or preferably, to unbend it. (206)

Nietzsche cautions against high-minded objectivism that depersonalizes the spirit in pursuit of “disinterested knowledge” (207). In this regard, the ideal scholar is no more than a mirror that submits itself before what is to be known, there is no ‘him’, no ‘person’, no ‘individual’, he is some glad little cog, sans individualité, cannot bear to answer ‘Yes’ or ‘No’. He is an instrument! A sublime slave, a tool to measure phenomena, a pot waiting to be filled that conforms to its contents. This type of man ought not be confused with the philosopher. (208)

The mixing of classes and races, hence values and moralities, breeds a skepticism that paralyzes Europe with a “sickness of the will” (208). In late 19th century Europe, Nietzsche sees the impending crisis of the co-habitation of multiple moralities and the dilemma of moral relativism, as well as the resurgence of those with a sufficient strength of will who can conquer these sentiments. Today we are more mixed, more confused, more righteous, than ever.

At the same time, Nietzsche praises a different type of skepticism: a “virile”, strong skepticism, that doubts but is not paralyzed by its doubts, that pushes back against the herd and the temptation of romanticism, to forge an independent spirit and acquire a dangerous freedom (209), to reject the feeble “democratic feelings” or idealistic “elevation by Truth” (210).

This skepticism despises and nevertheless seizes; it undermines and takes possession; it does not believe but does not lose itself in the process; it gives the spirit dangerous freedom, but it is severe on the heart… (209)

The philosopher must resist the temptation to specialize, he must attain a wide view as seen from above, above the scientific specialists, critics and “axiomizers”, who are mere tools. The philosopher may undergo several stages of development (artist, critic, skeptic, poet, etc.) to arrive at new heights, but his ultimate task is to use his wide perspective of values in order to create new ones.

[The philosopher] himself must have been critic and skeptic and dogmatist and historian and also poet and collector and traveler and solver of riddles and moralist and seer and “free spirit” and almost everything in order to pass through the whole range of human values and value feelings and to be able to see with many different eyes and consciences, from a height and into every distance, from the depths into every height, from a nook into every expanse. But all these are merely preconditions of his task: this task itself demands something different—it demands that he create values. (211)

Philosophers are not idle thinkers, their knowledge and world view compels them to creation and action, to rule and shape the future.

Genuine philosophers, however, are commanders and legislators: they say, “thus it shall be!” They first determine the Whither and For What of man and in so doing have at their disposal the preliminary labor of all philosophical laborers, all who have overcome the past. With a creative hand they reach for the future, and all that is and has been becomes a means for them, an instrument, a hammer. Their “knowing” is creating, their creating is a legislation, their will to truth is—will to power. (211)

The philosopher is a man of tomorrow, and so, conflicts with today. He vivisects present virtues and values to uncover “how many lies lay hidden under the best honored type of their contemporary morality, how much virtue was outlived” (212). He must have the individual hardness to go against “modern values”, against herd morality and the equality of rights, which seeks to eliminate all elevation and individuality.

Today, conversely, when only the herd animal receives and dispense honors, when “equality of rights” could all too easily be changed into equality in violating rights—I mean, into a common war on all that is rare, strange, privileged, the higher man, the higher soul, the higher duty—today the concept of greatness entails being noble, wanting to be by oneself, being able to be different, standing alone and having to live independently. And the philosopher will betray something of his own ideal when he posits: “He shall be greatest who can be loneliest, the most concealed, the most deviant, the human being beyond good and evil, the master of his virtues, he that is overrich in will. Precisely this shall be called greatness: being capable of being as manifold as whole, as ample as full.” And to ask it once more: today—is greatness possible? (212)

The philosophical state derives from experience unknown to most scholars, it cannot be taught. It is a state of necessity, not desire or voluntary action, that impels them to attempt the highest questions. As a believer in the heredity of acquired characteristics, Nietzsche suggests that genetics (“blood”) may play a role in the creation of a philosopher, and that the philosopher must necessarily be the result of the cultivation of many generations. (213)

Kaufmann suggests that much of Nietzsche’s thoughts of ‘elevated nobility’ was influenced by Aristotle’s greatness of the soul—megalopsychia—greatness which is self-determined and self-justified, and is accompanied by a sense of elevation and pride (Aristotle, Nichomachean Ethics, IV.3). Nietzsche also invokes the ‘great’ image of Socrates, who incessantly questioned the traditional virtues of Athenian citizens, but in his self-reflection realized that he was a man of his time as well and could not escape the irrationality which he criticized in lesser men.

Our Virtues

Moralists are limited in spiritual capacity, hence they seek a ‘lower’, herd-like morality before which they assume all to be equal.

Moral judgments and condemnations constitute the favorite revenge of the spiritually limited against those less limited … It pleases them deep down in their hearts that there are standards before which those overflowing with the wealth and privileges of the spirit are their equals: they fight for the “equality of all men before God” and almost need faith in God just for that. (219)

The ultimate product of moral qualities is “high spiritualization” of grace, justice and severity across generations through discipline and exercise, which separates and ranks men (219).

Nietzsche criticizes the”disinterest” in disinterested action and knowledge. There is no such mythical, transcendental disinterest, such an unegoistic sacrifice; there is always something of interest for the self (220). It is commonplace for on to proclaim himself free of self-interest and then purport to speak for the universal good of mankind:

Every unegoistic morality that takes itself for unconditional and addresses itself to all does not only sin against taste: it is a provocation to sins of omission, one more seduction under the mask of philanthropy—and precisely a seduction and injury for the higher, rare, privileged. Moralities must be forced to bow first of all before the order of rank; their presumption must be brought home to their conscience—until they finally reach agreement that is immoral to say: “what is right for one is fair for the other.”(221)

Higher and lower moralities ought to not be treated the same—the order of rank of values can be discerned by a people’s “historical sense” by which they know in their collective subconscious the relation between different value systems that existed and went extinct, that competed and combined with each other. The collective memory of all forms of “semi-barbarism” allows a people to endure suffering that to other peoples with younger memories (or no memories) would be absurd and unbearable (though naïvety may be more useful than learned helplessness). Nietzsche claims that the “democratic mingling of classes and races” (in nineteenth century Europe) accelerated the chaotic accrual of this instinct (224). Today, the hyper mixing and co-existence of disparate cultures erases the unique “historical sense” of a people and reduces moral thought to the lowest common denominator, e.g. ‘do-no-harm’ utilitarianism.

The lower moral systems are those “that measure the value of things in accordance with pleasure and pain”, that preach a universal pity with all suffering, that diminish man by denying his suffering, that want to abolish suffering altogether—a world where no one suffers would be the greatest possible achievement in this system! The tender feelings of pity followers of this system exhibit is for the “creature” in man—the pitiful, suffering animal—and not the transcendent creator that he has the potential to be. (225)

It is the discipline of suffering that has created all enhancements of man, that is responsible for the greatness in man, that is the fundamental essence of the oldest of religions. In the face of suffering, man cultivates virtue under a great tension of the soul in the form of courage, perseverance and profundity. (225)

Suffering also differentiates greater and lesser men; on the other hand, utilitarianism, which seeks to bring the most happiness to the greatest number of people and promotes the general welfare as the highest virtue, is incapable of recognizing the order of rank between men and moralities. Moreover, it lowers everyone to the same pitiable level. (228)

“[T]he general welfare“ is no ideal, no goal, no remotely intelligible concept, but only an emetic—that what is fair for one cannot by any means for that reason alone also be fair for others; that the demand of one morality for all is detrimental for the higher men; in short, that there is an order of rank between man and man, hence also between man and morality. (228)

I venture to think that even among moralities, utilitarianism would be ranked lower than the grand old Judeo-Christian tradition: the former explains away humanity in rational egoism and immanent utility, and is suspicious of non-useful conceptions of the good, whereas the latter recognizes the fundamental irrationality man and takes away his power to decide what is good and bad.

On Cruelty and the Spirit

The anti-humanist ‘warrior ethic’ with which Nietzsche is associated is presented here as the spiritualization of cruelty for group survival. From hunter-gatherer tribes to agricultural communities to poleis, empires and nation-states, most of what is considered to be “noble” are the cruel practices that have allowed its practitioners to survive together, often at the expense of individuals or other groups. Courage and cowardice is a classic example: without this socially-imposed vice and virtue, the most cowardly of the strongest group of people—”free-riders”—shall benefit the most. Such a society will perish. (Haight, The Righteous Mind)

Almost everything we call “higher culture” is based on the spiritualization of cruelty, on its becoming more profound: this is my proposition. That “savage animal” has not really been “mortified”; it lives and flourishes, it has merely become—divine. (229)

Cruelty is not simply the enjoyment of the suffering of others, it is also the enjoyment of one’s own suffering, as seen in the religious ecstasies of self-denial, self-accusation and repentance. An example is the knowledge-seeker who is cruel to his own intellectual conscience by forcing himself to recognize and explore things that are against his world view, against the “basic will of the spirit” (229).

This will to mere appearance, to implication, to masks , to cloaks, in short, to the surface—for every surface is a cloak—is countered by that sublime inclination of the seeker after knowledge who insists on profundity, multiplicity, and thoroughness, with a will which is a kind of cruelty of the intellectual conscience and taste. (230)

The spirit wants to be master of itself. It simplifies the external world by appropiating experiences in its existing framework of understanding and ignoring those which cannot be perceived in this way. In this regard, all experiences reinforce the self and produce the feeling of growth. Truth-seeking and love of knowledge is cruelty against the basic will of the spirit, which prefers masks and simplicity, and belongs to the human vanity that attempts to hide the fundamental nature of man.

Who can stand these enticing whispers: “You are something more”? Who can ask themselves: “Why have knowledge at all?” (230)

On Woman

Kaufmann, a modern academic, intelligently distances himself from Nietzsche’s thoughts on women. I, neither modern nor an academic, will take no such heed.

Nietzsche prefaces by recognizing that his thoughts belong to a fundamental prejudice, that the “truths” he speaks about woman are really only his truths:

[A]t the bottom of us, really “deep down,” there is, of course, something unteachable, some granite of spiritual fatum, of predetermined decision and answer to predetermined selected questions. … At times we find certain solutions of problems that inspire strong faith in us; some call them henceforth their “convictions.” Later—we see them only as steps to self-knowledge, signposts to the problem we are—rather, to the great stupidity we are, to our spiritual fatum, to what is unteachable very “deep down.” (231)

Nietzsche mocks the “democratic” movement of woman in the same manner he mocks “modern values”. He venerates traditional femininity, embodied by the Faustian Eternal-Feminine, as the most powerful and elevated state woman may achieve, and views the progression of woman’s rights, her self-determination and “modern” education as a regression of her form. It is important to note that the view that there exists transcendent masculine and feminine forces ought not to be conflated with gender essentialism, which claims that men and women are fundamentally distinct due to their biology, or that men must only embrace masculinity and women must only embrace femininity. To me, one can embody masculinity and femininity to different degrees. Nietzsche’s social critique draws from these two views:

Since the French Revolution, woman’s influence in Europe has decreased proportionately as her right and claims have increased; and the “emancipation of woman,” insofar as that is demanded and promoted by women themselves (and not merely by shallow males) is thus seen to be as an odd symptom of the increasing weakening and dulling of the most feminine instincts. There is stupidity in this movement, an almost masculine stupidity of which a woman who has turned out well—and such women are always prudent—would have to be thoroughly ashamed. (239)

Nietzsche wants to preserve the “natural” notion of woman as a dangerous, suffering figure against the “modernization” of women through education and “cultivation”, which destroys unique female figure in return for a boring (but ‘equal’!) tool that is inseparable from man. Just like how he attacked the democratic movement as “the general uglification of Europe” (232), he regards the “emancipation of woman” as a “defeminization” and “borification” (239). Woman ought to be different from man, but in the modern age, all differences are unfair!

To be sure, there are enough imbecilic friends and corrupters of woman among the scholarly asses of the male sex who advise woman to defeminize herself in this way and to imitate all the stupidities with which “man” in Europe, European “manliness,” is sick: they would like to reduce woman to the level of “general education,” probably even of reading the newspapers and talking about politics. Here and there they even want to turn women into freethinkers and scribblers. (239)

Great women arise from the force of their will, not from their modernization:

Altogether one wants to make [woman] more “cultivated” and, as is said, make the weaker sex strong through culture—as if history did not teach us as impressively as possible that making men “cultivated” and making them weak—weakening, splintering, and sicklying over the force of the will—have always kept pace, and that the most powerful and influential women of the world (most recently Napoleon’s mother) owed their power and ascendancy over men to the force of their will—and not to schoolmasters! (239)

More broadly, Nietzsche regards slavery as “a condition of every high culture, every enhancement of culture”, and in particular, considers the restraint and subservience of women a similar necessity (239). (However, in this context, “high culture” cannot be taken blindly as moral justification for what it entails: namely, cruelty.) Does the increase of culture necessarily lead to what we consider today as ‘regressive’ or ‘cruel’ acts, or vice versa? Would this have happened in the state of nature?

It seems to me that the reverence for traditional culture and grand morality is a poor counterargument to modern liberalism. Conservative traditionalism is a distant nostalgia and, at best, a parochial institution; it cannot function as a universal governing system of disparate peoples and cultures.

This begs the question: Is there an end to liberalism? Will modern society and morality (not modern law) be capable of restraining anyone in any way anymore?

Peoples and Fatherlands

Nietzsche returns to a critique of a group of people whose criticism we modern readers are more willing to accept: Germans.

The great statesman, Bismarck, in order to prepare his people for power, replaces their old virtues with coarse nationalism and narrow ‘politicking’. Such people will become strong, but not great; they will be shallow in their spirit, and their spiritual “flattening” will be contrasted with the “deepening” of another group of people (241). The strong may be ruled by the stronger, but the great will never be subjugated, not even to the ‘greater’.

Who will be the tyrant to master the strong? The conditions of democratization that make men into herd animals are also conducive to spawning dangerous individuals that can cultivate the vast diversity of cultures and spirits to their advantage. Meanwhile, the industrialization of man, that is, his reduction to a common tool or useful worker, prepares him for “slavery in the subtlest sense”, that is—slavery in the name of human progress. (242)

Nietzsche channels his inner Oogway：”[Germans] belong to the day before yesterday and the day after tomorrow—as they yet have no today” (240).

The German soul is above all manifold, of diverse origins, more put together and superimposed than actually built: that is due to where it comes from. A German who would make bold to say, “two souls, alas, are dwelling in my breast” would violate the truth rather grossly or, more precisely, would fall short of the truth by a good many souls. As a people of the most monstrous mixture and medley of races, … the Germans are more incomprehensible, comprehensive, contradictory, unknown, incalculable, surprising, even frightening than other people are to themselves: they elude definition and would be on that account alone the despair of the French. (244)

Nietzsche’s description of Germans may seem like a far cry from the Germans we see today (rarely do we think of Germans as possessing “diverse origins” or manifold souls), but there is truth in this origin of the eclectic mix of the Germanic and Prussian folk due to their late unification during the twilight of European nationalism. In the late 19th century, the German state, at last, rose from the ashes of the Holy Roman Empire to become its on independent nation, as with Italy and the Roman Catholic Church. As a result, Germans have no longstanding, innate definition, they are cloudy and unclear, they experience and develop:

[T]he German loves clouds and everything that is unclear, becoming, twilit, damp, and overcast: whatever is in any way uncertain unformed, blurred, growing, he feels to be “profound.” The German is not, he becomes, he “develops.” (244)

Nietzsche describes the Germans in paradoxical ways: Germans are contradictorily “[g]ood-natured and vicious”; they have a “boorish indifference to ‘taste’”, and a pondering outlook and slow digestion of events; they lack spiritual contemplation, yet have their own type of profundity. Perhaps the best way to understand Nietzsche’s reflections on Germans would be not to look at modern Germans or even Germans of the past, but rather, examine their spirit in distilled form, such as in the works of Goethe. (244)

There are words of Goethe in which he deprecates with impatient hardness, as if he belonged to a foreign country, what the Germans take pride in: the celebrated German Gemüt he once defined as “indulgence toward the weakness of others as well as one’s own.” (244)

The German does not discern art in either sentences he reads or the rhetoric he hears, save for that which comes from the pulpit:

In Germany the preacher alone knew what a syllable weighs, or a word, and how a sentence strikes, leaps, plunges, runs, runs out; he alone had a conscience in his ears, often enough a bad conscience; for there is no lack of reasons why Germans rarely attain proficiency in rhetoric, and almost always too last. The masterpiece of German prose is therefore, fairly enough, the masterpiece of its greatest preacher: the Bible has so far been the best German book. Compared with Luther’s Bible, almost everything else is mere “literature”—something that did not grow in Germany and therefore also did not grow and does not grow into German hearts—as the Bible did. (247)

Nietzsche portrays the German people as a masculine fertilizer of culture, as opposed to a feminine birther and nurturer of culture:

There are two types of genius, one which above all begets and wants to beget, and another which prefers being fertilized and giving birth. Just so, there are among peoples of genius those to whom the woman’s problem of pregnancy and the secret task of forming, maturing, and perfecting has been allotted—the Greeks, for example, were a people of this type; also the French—and others who must fertilize and become the causes of new orders of life—like the Jews, the Romans, and, asking this in all modesty, the Germans? (248)

It is remarkable that during the period of rising German nationalism, Nietzsche goes against the grain by claiming that the Germans are still a “weak and indefinite people”, and cannot “digest” the existence of a stronger group of people, such as the Jews, for fear of being extinguished by them (251). Nietzsche’s partiality towards the Jews is partially a result of his reverence for their “grand style of morality” (250), Old Testament virtues and their steadfastness in the face of ‘modern values’:

The Jews, however, are beyond any doubt the strongest, toughest, and purest race now living in Europe; they know how to prevail even under the worst conditions (even better than under favorable conditions), by means of virtues that today one would like to mark as vices—thanks above all to a resolute faith that need not be ashamed before “modern ideas” … (251)

Compared to the cohesion and unity of the Jewish race, the European nation is contrived and disunited. The Jews could very well have “literal mastery over Europe”, that they choose not to do so is a sign of their high elevation (251).

Despite all this severe criticism of Germans and praise of Jews, Nazi scholars deliberately misinterpreted Nietzsche to fit their own ideology, hence creating the uninformed perception of Nietzsche as a ‘Nazi’ philosopher that unfortunately persists to this day.

What does Nietzsche think of the English? Not far from a Napoleon’s “a nation of shopkeepers”, though the diligent empiricists may be more suitable than “free spirits” for discovering scientific truths, as the latter not only must acquire new knowledge but also embody and actualize the new:

There are truths that are recognized best by mediocre minds because they are most congenial to them; there are truths that have charm and seductive powers only for mediocre spirits … It would be a mistake to suppose that the spirits of a high type that soar on their own paths would be particularly skillful at determining and collecting many small and common facts and then drawing conclusions from them: on the contrary, being exceptions, they are from the start at a disadvantage when it comes to the “rule.” Finally, they have more to do than merely to gain knowledge—namely, to be something new, to signify something new, to represent new values. Perhaps the chasm between know and can is greater, also uncannier, than people suppose: those who can do things in the grand style, the creative, may possibly have to be lacking in knowledge—while, on the other hand, for scientific discoveries of the type of Darwin’s a certain narrowness, aridity, and industrious diligence, something English in short, may not be a bad disposition. (253)

Nietzsche contrasts the tastelessness of the Germans and the “plebeianism” of the English with the noble taste of the French, whom he claims to be the originator of European nobility and highness.

European noblesse—of feeling, of taste, of manners, taking the word, in short, in every higher sense—is the work and invention of France; European vulgarity, the plebeianism of modern ideas, that of England.— (253)

The French should have good reason to resist the “spiritual Germanization” of the soul: their capacity for art, their vielle culture moraliste (signifiant ‘auteur de réflexions sur les mœurs, la nature et la condition humaines’) and their combination of northern and southern characters allows them to rise above the crude and single-tuned fatherlandishness—”the disease of German taste” (254).

Nietzsche concludes the discussion of peoples and fatherlands with the claim that the greatest men of the 19th century have directed themselves towards the creation of a new Europe, a united Europe, and rarely do they lapse back into the modern waves of patriotism. European culture is deeply interconnected: even Wagner’s nationalistic music can be traced to French romanticism and other “supra-German” sources. (256)

What is Noble

In the final chapter of the book, Nietzsche revisits and expounds his theory of master and slave moralities.

Every enhancement of the type “man” has so far been the work of an aristocratic society—and it will be so again and again—a society that believes in the long ladder of an order of rank and difference in value between man and man, and that needs slavery in some sense or other. (257)

Nietzsche lauds the “pathos of distance” that exists between the ruling class and commoners as a form of alienation that nurtures the noble soul into its highest, most comprehensive form. However, Nietzsche acknowledges that the origins of aristocratic society are barbaric and cruel. The ruling class comes to power through their strength of will and desire for power, and by imposing their will and power on those “weaker, more civilized, more peaceful” groups of people:

[T]he noble caste was always the barbarian caste: their predominance did not lie mainly in physical strength but in strength of the soul—they were more whole human beings (which also means, at every level, ‘more whole beasts’) (257).

Over the course of the past few centuries, the aristocratic class has undergone a gradual self-demotion to a mere government function, that culminates, (from a tender fit of moral feelings) in the surrender of all their noble privileges. Nietzsche claims the purpose of the aristocracy is not to be functional or to serve society. On the contrary, society exists to elevate select individuals to a higher plane of being, and these individuals must, without any moralistic doubt, accept their natural ascension over others and their existence as self-fulfilling in meaning. (258)

A modern alternative to aristocratic society is to treat the sentiment of mutual non-harm as the basis of a society. However, Nietzsche claims that life itself is fundamentally suffering, oppression and exploitation, and the primordial will to power that seeks to grow, dominate and exploit is the basic function of life. A society that is based on mutual refrain from harm and exploitation cannot function as it attempts to deny and legalize away the essential will of life. (259)

Master and Slave Moralities

Though Nietzsche attempts to go ‘beyond good and evil’, it is difficult to read his comments on nobility and master morality without discerning the hue of praise and his comments on slave morality and modern ideas without the tone of disdain.

In master morality, the ruling class considers itself to be the creator of values, the decider of what is good, what is noble and what is contemptible. The noble man determines values, determines its own value, and judges; he distances himself from the common men and limits his interactions with them.

The noble type of man experiences itself as determining values; it does not need approval; it judges, “what is harmful to me is harmful in itself”; it knows itself to be that which first accords donor to things; it is value-creating. Everything it knows as part of itself it honours: such a morality is self-glorification. (260)

The noble type of pity from those that look down is different from the pity of sufferers or those who preach to them, a “pathological sensitivity and receptivity to pain; also a repulsive incontinence in lamentation, an increase in tenderness that would use religion and philosophical bric-a-brac to deck itself out as something higher”. This pity has no value and is, in short, “unmanliness” (293).

Egoism—the faith of self-determination—belongs to the noble soul. The noble soul accepts its existence and superiority and the subordination of others without any doubt, and “quite generally it does not like to look ‘up’—but either ahead, horizontally and slowly, or down: it knows itself to be at a height” (265).

In master morality, masters may behave any way they wish to lower beings, but respect their peers and foes, that is, whoever can harm them. The “instinct for rank”, the ability to recognize one’s peers, is a sign of high rank itself, and only with their equals do they consider mutual honours and rights (263).

In slave morality, the violated and the oppressed are skeptical of noble values, and the possibility of a higher “good”. They instead promote values that ease suffering and benefit survival: pity, patience, friendliness, willingness to help—essentially a “morality of utility”. The vain slave desires good opinions of him that he does not even have of himself, whereas the noble self-justify their worth. In slave morality, those who inspire fear in the herd are evil, whereas in master morality, good are those who inspire fear, the contemptible—cowards, opportunists, liars, flatterers—are bad. The slave longs for the happiness of freedom, where the master devotes himself to reverence. (260, 261)

A species comes to be, a type comes fixed and strong, through the long fight with essentially constant unfavorable conditions. Conversely, we know from the experience of breeders that species accorded superabundant nourishment and quite generally extra protection and care soon tend most strong toward variations of the type and become rich in marvels and monstrosities (including monstrous vices). (262)

At the surface, the two paradigms ‘hard times create strong men’ and ‘favourable conditions breed exceptional beings’ seem to be paradoxical. Once conditions grow more favourably, the new generation does not experience the ‘tension’ that the previous generations experienced, the old virtues are no longer a necessity, but a luxury, and so the entire generation as a whole will cease to be ‘hard’.

A new morality that talks of dignity and neighbourly love will arise, its ultimate goal is to make everyone similarly mediocre, only revere the mediocre. Yet, it is under these ‘favourable’ conditions which cause men to become more alike that certain individuals manifest and separate entirely from the herd, to create new values, to embody new concepts, to have their greatness appear on the horizon. The danger lurks not with the external world or any group but within each individual, each neighbour and friend, each heart, down to the very core.

“Profound suffering makes noble; it separates” (270). The higher man who has suffered looks down upon those who have not suffered in equal, he puts on masks to prevent exposing himself and receiving pity from those that know nothing.

This suffering man receives from woman, who is “clairvoyant in the world of suffering” and “desirous far beyond her strength to help”, eruptions of loving pity (or pitying love), which she believes to be omnipotent in saving all people. In this regard, she is similar to the figure of Christ:

It is possible that underneath the holy fable and disguise of Jesus’ life there lies concealed one of the most painful cases of the martyrdom of knowledge about love: the martyrdom of the most innocent and desirous heart, never sated by any human love; demanding love, to be loved and nothing else, with hardness, with insanity, with terrible eruptions against those who denied him love; the story of a poor fellow, unsated and insatiable in love, who had to invent hell in order to send to it those who did not want to love him—and who finally, having gained knowledge about human love, had to invent a god who is all love, all ability to love—who has mercy on human love because it is so utterly wretched and unknowing. Anyone who feels that way, who knows this about love—seeks death. (269)

The rites of religion, holy places of worship and forbidden acts all demonstrate cleanliness (or sanctity according to moral psychologists) in the separation of high and low, in that there are things you ought not to touch. Priestliness and saintliness are simply the spiritualization and refinement of cleanliness beyond antimicrobial practices (271). Modern thinkers seem to have discarded this innate sense in a fit of feeling of unfairness (they ask, naively, ‘Why?’) as they touch, lick and finger the nooks and crannies of everything. There is more nobility in the peasants who revere at a distance and know not to come too close. (263)

We trend towards an age of increasing commonality, but what, in the end, is common? A common language is not enough, we must have common experiences to understand one another, to cooperate, to reach consensus quickly in times of danger. Here Nietzsche hints at group selection:

[E]asy communicability of need… must have been the most powerful of all powers at whose disposal man has been so far. The human beings who are more similar, more ordinary, have had, and always have, an advantage; those more select, subtle, strange, and difficult to understand, easily remain alone, succumb to accidents, being isolated, and rarely propagate. One must invoke tremendous counter-forces in order to cross this natural, all to natural progressus in simile, the continual development of man toward the similar, ordinary, average, herdlike—common! (268)

To become an independent “free spirit” is to go against the grain of natural evolution. Surely we cannot all be so elevated, otherwise we would go extinct! Thus, the nobler man is at greater risk of perishing from a loss than a lower man (“In a lizard a lost finger is replaced again; not so in man” [276]). The ruination of the higher man is commonplace, everywhere lies pits for him to fall in, how many of these individuals have been lost to history! What we lesser beings remember is not the tragic hero, nor his inner hopelessness, not the individuals but idols—whose fictions we venerate, whose successes supersede themselves and serve a greater purpose, who are etched into the historical memory of a people.

The human being who strives from greatness considers everything along the way to be a means, delay or obstacle. His height begets graciousness towards his fellow man, all his relations are comedy, all his judgements full of irony, everything everyone is temporary but him—he is solitary. (273)

Beginning of the End

As Nietzsche begins to end his book, I begin to end this post, alas, “[t]he melancholy of everything finished!” (277)

Men of profound sadness betray themselves when they are happy: they have a way of embracing happiness as if they wanted to crush and suffocate it, from jealousy: alas they know only too well that it will flee. (279)

It takes noble self-control to praise only where one disagrees, this often leads to misunderstandings by the vain and common—”He praises me: hence he thinks I am right” (283).

The four virtues are courage, insight, sympathy, and solitude. Why solitude? It arises out of an urge of cleanliness to remove oneself from the filth of society, community and all things common. (284)

Great events, like light from distant stars, are not experienced and comprehended when they ‘occur’ (285), they occur at different times to different observers.

The hermit-skeptic will doubt that any philosopher has ever expressed his truest and deepest opinions in his writings, he thinks to himself: “does one not write books precisely to conceal what one harbors?” There must be something deeper, beneath the surface, there must be a philosophy behind the philosophy. (289)

The philosopher as the wretched soul, the reclusive sufferer:

Every profound thinker is more afraid of being understood than of being misunderstood. The latter may hurt his vanity, but the former his heart, his sympathy, which always says: “Alas, why do you want to have as hard a time as I did?” (290)

A philosopher—alas, a being that often runs away from itself, often is afraid of itself—but too inquisitive not to “come to” again—always back to himself. (292)

Transcendental irony is the mark of a higher philosopher: the ability to laugh at the world, at others, at oneself. (294)

Man invented morality to simplify his inner manifold so that he may withstand the sight of his own soul! (291)

Genius of the heart, silencer of the self-satisfied, pied-piper luring away the children, Socrates corrupting the youth—the (at)tempter, god of grape-harvest, wine, winemaking, fertility, orchards and fruit, rituals, religious ecstasy, theatre, chaos and creativity, and his last disciple, Nietzsche. Make man stronger, more evil and more profound—and more beautiful. (295)

Through writing, these thoughts have become old, weary and mellow, some are even ready to become truths. There was once a time when these thoughts were young and bright in the morning of the mind; those wicked, multicoloured thoughts as they originally manifested will never be known to anyone else. (296)

From High Mountains: Aftersong

—Da seid ihr, Freunde!—Weh, doch ich bin’s nicht, Zu dem ihr wolltet?

—There you are, friends!—Alas, the man you sought You do not find here?

Ihr zögert, staunt—ach, dass ihr lieber grolltet!

You hesitate, amazed? Anger were kinder!

Nur wer sich wandelt, bleibt mit mir verwandt.

One has to change to stay akin to me.

Concluding Thoughts

Nietzsche’s anti-modernism and anti-liberalism stood out to me most strongly when I first read BGE. His repeated attacks on ‘scientific labourers’, modern ‘herd animals’, democrats, egalitarians, utilitarians, advocates of rights are self-indulgent. Nietzsche emphasizes independence of ‘free-spirits’, ‘virile’ masculinity and separation from society to counteract modernity. Most of all, he attempts to go beyond “good and evil”, which is not necessarily the same as going beyond “good and bad”. Nietzsche elaborates in On the Genealogy of Morality, which serves as an excellent sequel to BGE as he elucidates the metaphorical concepts that he laid down in the latter.

References

Aristotle, ., Ross, W. D., & Brown, L. (2009). The Nicomachean ethics. Oxford: Oxford University Press.
Dostoyevsky, F., Pevear, R., & Volokhonsky, L. (1995). Demons: A novel in three parts. Vintage Books.
Dostoyevsky, F., Pevear, R., & Volokhonsky, L. (1994). Notes from underground. Vintage Books.
Frankl, V. E. (2006). Man’s search for meaning. Beacon Press, Boston.
Nietzsche, F. W., & Kaufmann, W. (2000). Basic writings of Nietzsche. Random House International.
Haidt, J. (2013). The righteous mind: Why good people are divided by politics and religion. Vintage Books.
Machiavelli Niccolò, & Wootton, D. (1994). Selected political writings. Hackett Pub. Co.
Mackie, J. L. (John Leslie). (1977). Ethics : inventing right and wrong. Harmondsworth ; New York :Penguin.
Plato, & Allen, R. E. (2008). The republic. Yale University Press.
Plato, Cooper, J. M., & Hutchinson, D. S. (1997). Plato: Complete Works. Hackett.

Running ML Inference Services in Shared Hosting Environments

2021-09-29T00:00:00+00:00

I had the pleasure of presenting ‘Running ML Inference Services in Shared Hosting Environments’ at MLOps: Machine Learning in Production Bay Area Virtual Conference. This presentation was based off the 6 years of experience the Nextdoor CoreML team has productionalizing and operating 30+ real-time ML microservices.

Abstract

Running a ML inference layer in a shared hosting environment (ECS, K8s, etc.) comes with a number of unobvious pitfalls that have significant impact on latency and throughput. In this talk, we describe how Nextdoor’s ML team experienced these issues, discovered their sources and fixed them, and in the end received latency drops of a factor of 4, throughput increases of 3x and improved resource utilization (CPU 10% -> 50%) while maintaining performance. The main points of concern are request queue management and OpenMP parameter tuning.

What You’ll Learn

Why your load balancing algorithm matters
The importance of request queue timeouts for service recovery
What resources are actually being shared in a shared hosting environment

Modern Agile for Machine Learning

2019-08-09T00:00:00+00:00

I had the pleasure of presenting ‘Modern Agile for Machine Learning’ at Toronto Machine Learning Micro-Summit Series to 100+ industry Machine Learning practitioners. The talk was based off my personal experiences in applying Extreme Programming practices for enterprise ML projects at Dessa.

Abstract

The world of enterprise Machine Learning is vastly different than that of small-scale use cases and kaggle competitions. Inexperienced ML teams that are used to the latter will encounter endless problems when they attempt to implement production-grade ML models in enterprise.

Many ML teams heavily focus on model performance and fail to follow standard coding practices, ultimately sacrificing code quality in favour of quick results. While this workflow is manageable for small projects, it is not sustainable for productionalizing large-scale, collaborative ML pipelines in enterprise, as ML teams accumulate massive amounts of technical debt and high cost-of-change, which can paralyze progress and delay production efforts.

In this talk, I will introduce how machine learning teams can apply Modern Agile and Extreme Programming engineering principles, such as test-driven development and refactoring, to Machine Learning Development, in order to deliver high-quality, flexible ML solutions with low cost-of-change that will save them massive amounts of time during development and production.

Toronto Deep Learning Series — Google BERT

2018-11-28T00:00:00+00:00

I had the pleasure of presenting the state-of-the-art NLP model, Google BERT, at the Toronto Deep Learning Series (TDLS) meetup. This video has 83000+ views as of Jun 2024, and is the top viewed lecture of TDLS (now called AISC).

Slides

From Academia to Industry

2018-10-09T00:00:00+00:00

In 2014, I entered the University of Toronto as an undergrad with a burning passion for physics. In 2018, I left the academic world to start a career in industry machine learning.

This is how I transitioned from academia to industry.

My Story
Advice
Conclusion
- Links

My Story

Discontent with Academia

I had loved physics ever since I read Stephen Hawking’s A Brief History of Time in the seventh grade. After studying the subject passionately in high school, I decided to go to the University of Toronto to study physics. My plan was to graduate with solid marks and excellent research experience, break into a top-tier physics PhD program and become a world-renowned physics researcher.

The only problem was that I didn’t like physics research.

It took two research internships in physics for me to realize that research was not the daily stream of intellectual delight that I had imagined it to be. Research was a grind. It was slow gruelling work that progressed inch by inch, and 90% of my time was spent programming.

I discovered that learning about a topic and doing research in that field were two completely different things, and although I loved to learn physics in a classroom, I disliked doing physics research.

In addition, I grew increasingly disillusioned with academia. It seemed that many STEM undergraduates would rush into PhD programs out of sheer inertia, without exploration of other options, and spend five, six years of their prime youth down the rabbit hole of academic research. This resulted in frequent burnouts. Many PhD graduates turned, in the end, to industry after acquiring a distaste for academia in grad school. Those who continued in academia were met with fierce competition for limited amounts of tenure-track positions, and must go through several rounds of postdoctoral research stints before being accepted for a faculty position, if they were accepted at all.

Academia is a pyramid, and tenured professors are at the top. For each professor, there are a handful postdocs, numerous graduate students, and many undergraduates to fill in the gaps, all wanting to climb the ladder. And I didn’t want to play this game.

At the end of the summer after my second year, I decided that research wasn’t for me. After that came a harder decision: what was I going to do next?

Initial Interest in Data Science & Machine Learning

I began by asking myself a simple question: What made me passionate about physics? I loved physics because it described the world mathematically and allowed us to make useful predictions about physical phenomena.

Then I asked myself: What other field allows me to make useful predictions about the world with mathematical models? After some exploration, I discovered that data science and machine learning (ML) satisfied my desire to mathematically describe the world. Compared to physics academia, it was more widely-applicable, and I didn’t need to commit years of my life in graduate school (and beyond) to make an impact. Furthermore, these highly technical fields were constantly covered in the media, and were even described by Harvard Business Review as ‘the sexiest job of the 21st century’. I decided to dig deeper.

I started learning ML with Andrew Ng’s Coursera course, which taught me high-level ML concepts. However, in order to really understand the fundamentals of ML, I needed to dive deeper into the theory and code. Stanford’s CS231n: Convolutional Neural Networks for Visual Recognition allowed me to do just that. The coding assignments, in which I implemented low-level neural networks in Python, gave me a solid understanding of the fundamentals of deep learning. I supplemented this course with another Stanford online course, Statistical Learning, which taught me the statistical side of ML.

What I discovered, surprisingly, was that the most widely-used ML techniques were quite simple and completely accessible to undergraduates with a basic background in mathematics and statistics. Other key components of data science, such as data preprocessing, exploration and visualization, were already familiar to me through past research experience.

Learn more about the resources I used here.

Additional skills include: Cloud computing, Statistics and Problem-Solving

Projects & Competitions

After acquiring the basic ML foundation, I wanted to apply what I had learned to solve real-world problems with real-world datasets. I first turned to kaggle competitions, doing them by myself initially and then together with the UofT Data Science Team.

Kaggle competitions were tough! Not only were the problems difficult, but the competition was fierce, and a lot of time must be devoted to compete for the top prizes. To me, an undergraduate student trying out ML, I decided not to invest large amounts of time in an attempt to win a competition, and simply treated kaggle as a learning opportunity.

Around this time, I discovered a cool, new data science hackathon in Toronto called HackOn(Data) that was to take place in a couple of weeks. HackOn(Data) was unique in that they organized hands-on workshops leading up to the hackathon, in which the participants implemented entire ML workflows, from data preprocessing to model validation, with industry datasets on Apache Spark. These small-classroom workshops not only taught me valuable, real-life skills, but also introduced me to the data science community in Toronto.

Then came the actual hackathon. Long story short, facing stiff competition from PhDs and industry professionals, my teammate and I finished third in the competition. More importantly, we made valuable connections with prominent industry leaders that resulted in internship opportunities the next summer.

My teammate Chris and I winning third place at HackOn(Data) 2016

Here, I learned an important lesson: Networking and community involvement are extremely important to career success, especially in hot, new fields such as ML and Data Science. Often, academics like me focus too much on domain skills and knowledge, and forget about the people in the domain, but the people are just as crucial to your career, if not more.

The rest of the story at HackOn(Data) 2016 here.

Internship

During my third year, I decided to try out industry by looking for an industry internship next summer. Through a contact from HackOn(Data), I found a summer internship opportunity at an industry lab called Zero Gravity Labs, which was the semi-independent, innovation arm of LoyaltyOne, the loyalty marketing company known for AirMiles. It sounded like a great way to test out industry life while working on cool projects, and I joined the Zero Gravity Labs team.

I love the spaceman

At Zero Gravity Labs, I explored decentralized loyalty programs on blockchain and created a loyalty blockchain Proof-of-Concept. I also worked with massive amounts of client data on Apache Spark and Microsoft Azure. Though the project did not contain ML, it gave me my first taste of working with large industry datasets, and I realized that it was much more difficult and cumbersome than working with the curated kaggle datasets.

During the same time, I worked on a side project in which I used xgboost to make residential real estate predictions. To increase my involvement in the data science community, I created data science workshops for UofT students with the UofT Data Science Team, gave a talk on setting up Spark with AWS, and served as a mentor and judge at HackOn(Data) 2017.

Industry or Grad School

At the end of summer, I realized that careers in industry could be just as fulfilling as careers in academia, and certainly lucrative. When I started my final year, I made the decision to go into the data science and machine learning industry. I would be the only one in my program to not continue in academia.

Many asked me if I would consider graduate school down the road. I responded that if I found an area that I passionately wanted to research, I would consider it. However, I would not go to graduate school out of sheer inertia, which is the current trend among undergraduates. In my view, many undergraduates go to graduate school simply because they have nothing better to do, and end up wasting years of their youth in prolonged over-education. The opportunity cost of that is huge: compare the five years it takes to complete a PhD versus five years of industry experience and wages, and ask yourself which is the better option? For me, it was the latter.

Job Search

When my fourth and final year began in September, I began my search for full-time, post-graduation employment. It was a long journey with many obstacles and deadends, that lasted all eight months of my final year.

This was how it went.

September, October — Light Searching and Reconnaissance

Surveyed the career market for ML and Data Science
Attended career fairs and networking events at UofT
Polished resume and LinkedIn profile
Started applying for new grad roles at big companies

I began my search by identifying interesting companies and gathering information on them: positions available, requirements and deadlines. Starting early before applications even began gave me a head start in surveying the landscape of career opportunities in Silicon Valley, Seattle, NYC and Toronto.

Having learned about the importance of networking, I attended various career events aimed at UofT new graduates, hosted by big companies like Microsoft, Google and Uber ATG. This gave me insight on role expectations and hiring processes.

I polished my resume and LinkedIn profile by following the advice in this article, written by the former SVP of People Operations at Google, Laszlo Bock. He taught me how to frame my achievements to make them stand out.

And by mid-October, when big tech companies started opening up applications for new graduate roles in May 2018, I was ready to start applying.

November, December — Applying, Interviewing, Pivoting

Low response rate from online applications
Realized online application was a black hole
Pivot to people-oriented approach

During November, I concentrated mainly on the big tech companies: Facebook, Google, Microsoft, Amazon, Airbnb, etc., with little success. These companies were simply not responding or giving automatic rejections. The only mild success was through the internal referral of a friend, which led to an interview for a Data Scientist role at a big Silicon Valley tech company. I passed the initial screen, but got stopped at the technical interview.

Frustrated and dejected, I realized that my current method of online applications was simply not working. Due to the huge volume of applications, I would be lost among the masses or be automatically filtered due to my lack of a graduate degree.

One day, I shared my troubles with a close friend that had good success in the tech industry, and asked him how he looked for career opportunities. To my surprise, he told me that he did not even bother with applications at all. Instead, he found all his opportunities through networking or cold calls.

Upon reflection, I realized that all the positive responses I have received were a result of personal connections and referrals. Attempts to make personal connections were more fruitful than online applications.

As a result, I decided to pivot to a people-oriented approach for job hunting, and invest more time in attending networking events and connecting with employers through email and LinkedIn.

January, February — Interviews, People-Oriented Approach

People-Oriented approach with focus on Toronto companies more successful
Completing technical challenges and interviews

I tested out my new strategy in the new year — the peak hiring stage for many companies.

With the new people-oriented approach, I received better responses and more interviews with big companies and startups in Toronto. Although this success may be partially attributed to my shift of focus towards Toronto companies (which are easier for Toronto candidates to enter), the new approach still made a difference in the quality of response.

Through a personal introduction, I could explain my situation and desires clearly, and have my achievements and experience in your profile for the other side to see. I found LinkedIn to be more successful than email as it offered a more in-depth profile and a sense of social familiarity. I did not neglect valuable face-to-face talks, and incorporated them in my process as well.

The downside to this approach was that it was more unstructured and disorienting than simply sending out applications. There was no set path of engagement, I had to define it myself.

By this time, it seemed unlikely that I was going to be able to break into the U.S. tech industry with my current skillset and experience, especially during a time of political uncertainty in the U.S. for high-tech work visas. I decided to switch my focus entirely to companies in Toronto.

March, April — Negotiation, Decisions

Waiting for offers
Evaluating market expectations for compensation
Negotiation and decisions

By March, I had finished all remaining interviews. In the next few weeks, the offers and rejections started rolling in, and I began to evaluate each offer. In order to discover the market expectations for each role, I researched comparable roles and their compensation levels, mainly by using online tools and surveys, such as Glassdoor and LinkedIn Salary, to find information for the tech industry in Toronto.

Then, I negotiated offers with company representatives, which worked well in some cases, poorly in others. Regardless of the outcome, negotiation was an important part of my job hunt, and I used this opportunity to try it out and hone my skills.

In the end, I decided to accept a Machine Learning Engineer position at a Toronto AI startup called Dessa. At Dessa, I would be working at the ground-level to develop and deploy ML models in big companies over disparate industries, such as finance and telecommunications. The biggest draw to Dessa for me was the ability to see how hyped ML techniques were actually implemented in large, complex enterprises. Additionally, I believed that the experience gained here would accelerate my knowledge and career growth.

Dessa discovers, develops and deploys AI solutions in enterprise

Advice

Attempting to transition from a completely academic role to an industry role is a daunting task. What makes it particularly disorienting is that there is no well-defined path, and therefore, you will need take initiative and explore many options.

So how can you get started? These key points, refined from my story, will help you.

Personal Projects

The age-old paradox of ‘How to get work experience if work requires experience?’ can easily be solved in the tech industry with projects (and internships). Personal projects can take many forms: kaggle competition, hackathon hack, capstone project, etc. Ultimately, it needs to demonstrate your curiosity and self-motivation to implement a novel idea, outside of regular coursework. I also recommend writing a blog post on your experiences and posting the project to github to give it more visibility.

Personal projects allow you to gain valuable experience — notches on your belt — that is crucial to getting your first job. You also will get a glimpse of what it is like to work in this field.

Networking

Do not fall into the trap of believing that just because you are in a technical field, that you can forgo networking. Who you know is just as important as what you know.

Nowadays, companies are increasingly looking towards universities for fresh talent, and are eager to meet students who are looking for a career in industry. In addition, reach out to professors, alumni and other students to get advice and information about career opportunities.

The value of these connections is high: You never know who may be the key to getting your foot in the door.

Job Hunting

The first job is always the hardest. Compared to an experienced candidate, you are perceived by companies to have lower value and higher risk. It gets easier once you have your foot in the door with that first job.

Start Early

Job hunting is a full-time job. Start early.

Starting early doesn’t mean sending out applications for jobs a year in advance. There is a lot of preparation you have to do before you can start applying. First, research industries and companies that you would like to join, and map out their roles and application timelines (new grad application deadlines are pretty early). Get in contact with company employees through LinkedIn and email to make a proper introduction, and express genuine interest in the work that they do.

Refine your resume to suit your desired companies. I recommend framing your achievements as ‘I accomplished [X] as measured by [Y] by doing [Z] (source).’This format is powerful in that it allows you to state your accomplishments and how they resulted in measurable benefits.

Looking for a job itself is a full-time job. And it’s exhausting. Giving yourself a head start will allow you to explore more opportunities without being stressed. My job search, from initial scouting to signing the offer, took nearly all 8 months of my last year at university. Had I not started early, I would have explored less options and realized key mistakes too late.

Have Options

Don’t put all your eggs in one basket.

It is almost certain that you will be rejected by at least one company. Cast out a wide net, explore different options, and don’t aim for a single dream company. In tech, big companies in Silicon Valley, such as Google and Facebook, are very attractive to new graduates, but there are plenty of other excellent companies, some of which may even be a better fit for you.

Having choices at each stage of the job-finding process is crucial. At the start of your job search, staying flexible allows you to broaden the scope of your search and discover opportunities that you otherwise would not known. At the end, having multiple job offers gives you more career choices, as well as an increased ability to negotiate.

People-Oriented Approach

Don’t focus on applications, focus on people.

Companies receive massive amounts of online applications, and yours is likely to get lost in the masses. How do you stand out?

Given that you have a polished resume and presentable LinkedIn profile, find people in hiring roles or in roles similar to your desired one, preferably in a position of seniority or leadership — hiring managers, senior managers, senior engineers, etc. Then, contact them on LinkedIn or email to make a quick introduction and demonstrate interest in their work. In addition, try to get a better understanding about the company culture, the pace of work and opportunities for career development. Be genuinely excited at the opportunity to work at their company.

During my job search, I reached out to an AI lab to talk about a cool paper that they had published and asked about career opportunities there. To my surprise, they responded quite warmly to my genuine interest in their paper and scheduled a face-to-face chat. I was amazed at the level of response to personal messages, even from CEOs and CTOs (of startups), and I discovered career opportunities otherwise inaccessible.

So, go for it. Reach out to new people. And don’t forget about your existing network: The best career opportunities often come from the connections you already have, such as former classmates and co-workers.

Negotiate

Disagree without being disagreeable.

Many people dislike negotiation because of its implicit association with antagonism and bullying, the kind they see in TV dramas, or in street haggling. People feel especially uncomfortable in the very personal case of negotiating their own worth.

However, real, merit-based negotiation does not have to be a rude or unpleasant experience. You can make strong, level-headed cases about the market value of your skills and experiences based on other offers with similar roles. Resources, like compensation surveys (e.g. Canada New Grad Offers 2017-2018) and online tools (Linkedin Salary, Glassdoor) will help you to critically evaluate your market value.

If you observe a discrepancy between your perceived market value and the company’s value assessment of you, don’t be afraid to address it. Negotiate for the appropriate job responsibilities and compensation.

Here are two beginner books on negotiation that very job candidate should read:

Getting to Yes, Fisher and Ury, Harvard Negotiation Project
Never Split the Difference, Voss and Raz, FBI hostage negotiator

Conclusion

My transition from academia to industry was long and gruelling. There were times during the long stretch of job rejections when it was difficult to see the light at the end of the tunnel. I was exhausted and dejected, but I pushed on, hoping that it would eventually pay off. In the end, through non-conventional methods, I was able to find a great position at a local AI startup where I could gain valuable experience on applying ML in big enterprises.

Now, I look back on my experiences as a tremendous learning opportunity and a crucial part of my career journey: The realization that I was not suited for academia, the decision to go into industry and the strenuous search for a job after graduation. I have shared with you what I learned in past two years, from the importance of personal projects to the importance of networking, from resume improvement to negotiation tips, from low-value online applications to high-value personal contacts, and more …

Now I continue into the next chapter of my career.

I hope you find this useful.

Links

Find me on LinkedIn through the sidebar.

Cover illustration by Eliot Wyatt.

An Honest Look at UofT

2018-07-24T00:00:00+00:00

What is UofT like? Is it hard? Is it as depressing as people say it is? And what is POSt?

As a recent Bachelor of Science graduate, I answer these and more in this guide for new students.

What is UofT Like?

The University of Toronto is the oldest university in Canada and internationally renowned for its research and professional faculties. However, the research and prestige of a university do not always translate to a good undergraduate education.

At its best, UofT is a cornucopia of limitless resources, brilliant faculty and world-class research.

At its worst, UofT is a bureaucratic beast that entraps undergraduates with its difficult courses, harsh marking scheme and general indifference on student well-being.

Here are some crucial things to know about UofT.

Pathway to University College and Front Campus

You Aren’t Admitted into Your Program of Study Until Second Year

When you are accepted into UofT, you are put into a stream of study in first year, but you are not admitted into your Program of Study (POSt) until second year. Many of these POSts have enrolment requirements, such as prerequisite courses and minimum grades, and some even require formal applications and interviews. It can feel like getting into university all over again.

Huge first-year streams, which have been steadily growing over the past few years and are projected to keep growing, have made it more and more difficult to enter a competitive POSt. A notable example is the Computer Science Specialist: the minimum first-year grade requirement for the Computer Science Specialist POSt has risen from 50% in 2014 to 83% in 2018 [1].

The trend at UofT is to have easy requirements to enter university, but hard requirements to enter your POSt. Of course, for UofT, this generates more revenue while keeping the programs high-quality and elite. As a result, it is common to see students taking five or six years to graduate, rather than the usual four.

The College System

Arts & Sciences students at St. George campus are split into seven colleges, with each college acting as the primary provider of services for students, such as academic counselling, accommodation and scholarships, and also community activities., such as orientation week and socials. If you are looking to make friends and be involved in a community, your college is a great place to meet people from outside your program, as it is smaller and more intimate than the university at large.

Although you are guaranteed a spot in residence as a first-year, it may not be the residence of the college you selected. You may have the misfortune of being relinquished to the far confines of Chestnut Residence.

Victoria College, One of the Seven Colleges of UofT

Tough Marking

To control program entry, first-year prerequisite courses generally have a preordained range for class averages. In my experience, general first-year math courses have final class averages in the high 60s range, and that may be after a substantial portion of students have already dropped out. Although professors usually do not adjust grades downwards, they can choose to make final exams rather tough if the class average is too high.

The good news is that a grade of 85 at UofT is considered to be a 4.0.

Funnily enough, more advanced courses may be graded more easily and have higher class averages. Professors are willing to be more lenient given that harder material is taught, but keep in mind that students who take advanced courses are usually bright and ambitious.

Large Class Sizes

Be prepared for large class sizes. A general first-year course at UofT like Calculus and Biology can easily have over 3000 students, and a first-year lecture in Convocation Hall may have nearly 2000 students, which often leaves late students having to stand through the lecture! This is especially true for students in the Life Sciences stream, whose first-year course load consists almost solely of Con Hall lectures. The good news is that as you progress into your upper years, the courses become smaller and more specialized, some having just a handful of students.

Convocation Hall, Capacity: 1730

Large Commuter Population

UofT, like other universities in Toronto, has a huge commuter population: A survey in Fall 2015 found that over half of UofT students commute (2). With rising rental prices in downtown Toronto, fewer and fewer students have the financial means to be able to live close to campus.

Commutes of over an hour make it difficult for students to engage in extracurricular activities and social events on campus. Waking up early in the morning and always worrying about getting back home takes its toll on university life.

Large International Population

In 2018, international students make up 22.5% of the undergraduate population and 16.8% of the graduate population. A stunning 65% of international undergraduates come from China, while the next largest group is India at 4%. The number of international undergraduates is projected to increase while the number of domestic undergraduates is projected to decrease (3). This is no surprise as international tuition is much higher than domestic tuition.

Student Enrolment, Fall 2017 - 2018

If you are in Math, Stats, or CS, you may end up in the minority of domestic students and find it difficult to communicate with your fellow classmates, due to language barriers and cultural differences. The upside is that you get to meet people from around the world.

Little School Spirit

In addition to the points above, the crushing academic atmosphere and lack of a secluded campus result in low school spirit. This is at stark contrast to small-town universities like Queen’s and Western which have strong school spirits and cohesive communities.

Most UofT community activities are centred around individual colleges and faculties, and there, you will find tight-knit communities there. My advice for first-years who find themselves lost in the sea of students is to find small, local groups in their college or program and build from there.

Bureaucratic

Large organizations like UofT are inherently bureaucratic and slow to get things done. For example, you have to jump through many hoops just to see your final exam, and many more to request a regrade. The system can be frustrating for newcomers who do not know how to navigate it.

Research

If you want to get into academia or go to grad school, start research early. Unfortunately at UofT, due to its focus on high-level graduate research, it is difficult to receive a research position as a first-year. Other schools, however, are more lenient. In the summer after my first year, I was able to receive an NSERC USRA at the University of Waterloo to do astrophysics research, which would have been tough even for second-years at UofT. In the early years of undergraduate research, don’t just consider UofT, broaden your horizons.

Tips on Success

For courses, know your limit and don’t bite off more than you can chew. Don’t take a course load that is completely overwhelming, as it will stress you and impact mental health, and the learning experience will not be enjoyable.
UofT students tend to be rather negative about their school, their courses and their overall prospects. Don’t fall into the cycle of negativity, keep your head up and stay positive.
Mental health comes first: UofT students are known to be prone to mental health issues. Remember, courses can be retaken, they are not worth the degradation of your mental health.
Get to know the people in your classes and program, their support will prove invaluable to your success during school and after you graduate
Build relationships with your professors: reach out to them about coursework, research opportunities or simply to discuss interesting topics.
Start tasks early, because things pile up. I found that simply printing the assignment as soon as I received it encouraged me to start working earlier.
Be careful about skipping class, catching up after you’ve fallen behind is difficult and stressful.
Massive amount of resources available, but you have to find them and know how to use them, e.g. libraries, writing centres, mentorship programs
You can find past finals online and past A&S term tests at the Arts & Science Student Union – very helpful
Use summers wisely – don’t do nothing. Personally, I was never a big fan of summer school, I preferred finding research work or an internship.
Take care of your body. Healthy body, healthy mind. Eat well, exercise, and remember that UofT gyms are included in tuition.

Parting Thoughts

UofT is a research-oriented school that is tough on undergraduates. At UofT, you are exposed to world-class research environments and limitless opportunities, but it’s up to you to take advantage of these resources.

To me, UofT wasn’t the standard university experience, it was a place where I learned a great amount, made connections and started my career – that’s all. I don’t feel any emotional connection to UofT as a whole.

And that’s okay.

About Me

In 2014, I entered the University of Toronto with a burning passion to study physics. Halfway through my undergraduate degree, I became disillusioned by academia and decided to pursue industry opportunities. In 2018, I graduated with a B.Sc as a Math & Physics Specialist and started a career in Data Science and Machine Learning.

Spark Performance Tuning

2017-10-09T00:00:00+00:00

At ZeroGravityLabs, Taraneh Khazaei and I co-authored a fantastic blog post that details resolutions of common Spark performance issues. It was featured on the Roaring Elephant - Bite-Sized Big Data podcast.

Spark Performance Tuning: A Checklist

Machine Learning Resources

2017-07-15T00:00:00+00:00

I have made a list of useful ML resources and recommended study routes. This will be updated from time to time.

Check it out here.

Toronto Apache Spark 19

2017-06-03T00:00:00+00:00

I had the pleasure of presenting how to set up Spark with Jupyter on AWS at Toronto Apache Spark #19.

HackOn(Data) 2017

2017-06-02T00:00:00+00:00

HackOn(Data), Toronto’s very own data hackathon in the heart of downtown, is back for 2017!

At HackOn(Data) last year, I learned a lot, had lots of fun, and made industry connections that landed me and my teammate great summer internships (my blog post). This year I plan on volunteering for HackOn(Data) 2017.

I highly recommend HackOn(Data). Register at hackondata.com/2017!

University of Toronto Data Science Team

2017-04-21T00:00:00+00:00

The UDST Kaggle Team discussing strategies for the kaggle Data Science Bowl 2017

Over the past school year (2016-2017), I have been participating in kaggle competitions with the University of Toronto Data Science Team (UDST).

We have participated in competitions such as the Outbrain Click Prediction, DSTL Satellite Imagery Feature Detection and Data Science Bowl 2017. I have learned a lot from my participation in UDST. In fact, it was these competitions that led me to write my spark-Jupyter-AWS guide, and the posts on multi-cpu data processing and s3 data access with boto3.

If you are a UofT student or simply a data enthusiast in the Toronto area, come check us out! We will be continuing activities in the summer of 2017.

Multi-CPU Data Processing

2017-04-20T00:00:00+00:00

Using all 8 CPUs of an AWS EC2 c4.2xlarge instance. Keep an eye on your memory!

When the University of Toronto Data Science Team participated in Data Science Bowl 2017, we had to preprocess a large dataset (~150GB, compressed) of lung CT images. I was tasked with the following:

Read data from S3 bucket
Pre-process the lung CT images, following this tutorial
Write the pre-processed image array back to S3

For S3 I/O on python, see my other post. In order to analyze the data efficiently, I used the python package multiprocessing to maximize CPU usage on an AWS compute instance. The result: Multi-CPU processing on a c4.2xlarge was 6 times faster than ordinary pre-processing on my local computer.

Setup

This will use the default python package multiprocessing. See the official documentation.

Multiprocessing

Doing basic Multi-CPU processing, or multiprocessing in general, is quite simple.

To use multiprocessing, you must first create a function that each process will run. Then, simply start all the processes.

Below, we initiate 2 processes that will run on 2 CPUs, each running function with arguments (arg1, arg2):

import multiprocessing

p1 = multiprocessing.Process(target=function, args=(arg1, arg2))
p2 = multiprocessing.Process(target=function, args=(arg1, arg2))

jobs = [p1, p2] #This allows you to access p1, p2 later

p1.start()
p2.start()

You can scale this up to your liking, or initiate them in a for loop.

To pre-process the Lung CT Images data, I divided the data into 12 sections and ran 12 processes on a c4.2xlarge, with 8 CPUs and 16GB of RAM. The reason I ran 12 processes on 8 CPUs is because roughly a third of the pre-processing time is used to download and upload data, which frees up the CPU for another process. This way, I ‘overload’ processes to ensure that the every CPU is being used at all times.

The result was that the multi-CPU pre-processing was 6 times faster than normal processing. However, if we use more powerful instances, such as the c4.8xlarge, which has 36 CPUs, we can cut down our processing time even more.

There are a lot more complex things you can do with the multiprocessing package, and we have just scratched the surface here. I found this to be a simple, yet powerful tool, whose usefulness will grow as the power of cloud computing increases.

Click here for the notebooks and module I used to preprocess the Kaggle Data Science Bowl Data.

Accessing S3 Data in Python with boto3

2017-04-19T00:00:00+00:00

Working with the University of Toronto Data Science Team on kaggle competitions, there was only so much you could do on your local computer. So, when we had to analyze 100GB of satellite images for the kaggle DSTL challenge, we moved to cloud computing.

We chose AWS for its ubiquity and familiarity. To prepare the data pipeline, I downloaded the data from kaggle onto a EC2 virtual instance, unzipped it, and stored it on S3. Storing the unzipped data prevents you from having to unzip it every time you want to use the data, which takes a considerable amount of time. However, this increases the size of the data substantially and as a result, incurs higher storage costs.

Now that the data was stored on AWS, the question was: How do we programmatically access the S3 data to incorporate it into our workflow? The following details how to do so in python.

Setup

The following uses Python 3.5.1, boto3 1.4.0, pandas 0.18.1, numpy 1.12.0

First, install the AWS Software Development Kit (SDK) package for python: boto3. boto3 contains a wide variety of AWS tools, including an S3 API, which we will be using.

To use the AWS API, you must have an AWS Access Key ID and an AWS Secret Access Key (doc). It would also be good to install the AWS Command Line Interface (CLI) as it is the AWS API in the terminal.

Now you must set up your security credentials. If you have AWS CLI installed, simply run aws configure and follow the instructions. Else, create a file ~/.aws/credentials with the following:

aws_access_key_id = YOUR_ACCESS_KEY
aws_secret_access_key = YOUR_SECRET_KEY

See boto3 Quickstart for more detail.

There are two main tools you can use to access S3: clients and resources. Clients are low-level functional interfaces, while resources are high-level object-oriented interfaces. I typically use clients to load single files and bucket resources to iterate over all items in a bucket.

To initiate them in python:

import boto3

client = boto3.client('s3') #low-level functional API

resource = boto3.resource('s3') #high-level object-oriented API
my_bucket = resource.Bucket('my-bucket') #subsitute this for your s3 bucket name.

Reading and Writing Files

To read a csv file with pandas:

import pandas as pd

obj = client.get_object(Bucket='my-bucket', Key='path/to/my/table.csv')
grid_sizes = pd.read_csv(obj['Body'])

That didn’t look too hard. So what was going on? If you take a look at obj, the S3 Object file, you will find that there is a slew of metadata (doc). The 'Body' of the object contains the actual data, in a StreamingBody format. You can access the bytestream by calling obj['Body'].read(), which will read all of the data from the S3 server (Note that calling it again after you read will yield nothing).

In this case, pandas’ read_csv reads it without much fuss. However, other files, such as .npy and image files, are a bit more difficult to work with.

For example, to read a saved .npy array using numpy.load, you must first turn the bytestream from the server into an in-memory byte-stream using io.BytesIO. Make sure you have sufficient memory to do this.

from io import BytesIO

obj = client.get_object(Bucket='my-bucket', Key='path/to/my/array.npy')
array = np.load(BytesIO(obj['Body'].read()))

The method BytesIO(obj['Body'].read()) works for most files.

To upload files, it is best to save the file to disk and upload it using a bucket resource (and deleting it afterwards using os.remove if necessary).

my_bucket.upload_file('file',Key='path/to/my/file')

It also may be possible to upload it directly from a python object to a S3 object but I have had lots of difficulty with this.

Iterating Over Bucket

You will often have to iterate over specific items in a bucket. To list all the files in the folder path/to/my/folder in my-bucket:

files = list(my-bucket.objects.filter(Prefix='path/to/my/folder'))

Notice I use the bucket resource here instead of the client. You could run client.list_objects() with the same arguments but this query has a maximum of 1000 objects (doc).

files will now contain a list of s3.ObjectSummary objects that display a bucket_name and a key. To get the first object, simply run:

obj = files[0].get()

And you can read the data as in the above section.

Conclusion

You learned the basics of S3 data import and export, and how to programatically access files in a bucket using the Python API for AWS, boto3.

An example notebook on how to work with s3 data with boto3, for the kaggle DSTL Satellite Image Challenge.

My Experience as a TA

2017-03-22T00:00:00+00:00

I’ve always loved to teach.

In high school, I lead an AP Physics group for one and a half years in which I taught my fellow students the AP Physics B curriculum in preparation for the AP examination. I found teaching to be a very fulfilling experience. It added a new dimension of meaning to my knowledge: I didn’t learn just for my own sake but also so I may help others learn.

In my first year at the University of Toronto, I resolved to become a Teaching Assistant. At UofT, TAs mainly teach tutorials, small interactive classes that focus on the practical applications of concepts taught in lectures. I was motivated by my own experiences in tutorials: the usefulness of having a good TA and the frustration of having a bad one. I wanted to be that good TA.

So in the summer after my first year, I filled out the simple Undergraduate Teaching Assistant application form for the Dept. of Mathematics. I was accepted in early September, the week before class started.

No resume. No interview.

I was surprised at how simple it was. Naturally, I had good grades in the first-year specialist math courses — MAT157, MAT240/247 — but more importantly at the time, the math department was burgeoning with students. Growing enrolment numbers at UofT meant larger first-year classes, especially first-year math courses, which are common program requirements. This drove up the need for math TAs, to my benefit.

For my first teaching assignment, I was assigned to MAT223: Linear Algebra I, which despite its 200-level course code, is meant to be a first-year course (they do this because there’s a limit to how many 100-level courses you can take). I got a lot of questions about how a second-year student could TA a second-year course.

Teaching tutorials was a different experience than teaching my AP Physics group in high school. There were a lot more students and the environment was more serious, and hence, I was nervous and stumbled often. It’s definitely not easy to teach in a classroom setting: to go in front of a class of students and solve problems on the board, it requires much more than knowing just how to do the problems. After all, explaining to others is much more difficult than explaining to yourself.

I eventually got the hang of it.

Now I have been a teaching assistant for nearly two years, and I intend to continue in my last year at UofT. It has been a very rewarding experience to see students progress in their mathematical abilities and reasoning. Furthermore, explaining mathematical concepts also requires you to have a deep understanding of the material. Thus, teaching students has not only improved my own knowledge of mathematics but also allowed me to understand how students learn and where they may have difficulties with the material.

Of course, I still stumble from time to time. When you mess up on the board, there’s really nothing to do except to acknowledge your mistake and move on. Other times a student will ask a question that you don’t know the answer to. In such cases, it’s better to admit ignorance than to feign knowledge, for the benefit of both the student and yourself.

I constantly strive to improve my teaching. To do so, it is important to recognize areas where you lacking, and no one knows that better than your students. Anonymous feedback from my students helped me in identifying areas where I needed improvement that I had never thought of. It’s never pleasant receiving criticism (anonymity brings harshness), but it’s a crucial part of getting better.

Finally, the pay is good: ~$42/hour + benefits and additional funding. However, the pay rate $42/hour is a slightly misleading statistic, which the UofT administration didn’t hesitate to expound during the 2015 TA strike. Teaching Assistants are given contracts, which constitute a set amount of hours for the TA to work throughout the length of the course. These contracts are rigid, meaning that you typically cannot be paid more than what is stipulated on your contract. It is possible for a course coordinator to assign extra hours for new tasks but these assignments must be arranged in advance. It is not possible for a TA to get paid for extra hours retroactively. For example, if you are assigned 6 hours to mark an exam and you end up spending 10 hours marking (often the case), you will not get paid the additional 4 hours. Therefore, TAing is a great opportunity for Undergraduates like me to make some money on the side, but it is less appealing for graduate students who may depend on it as a main source of income.

All in all, it’s a lot of fun and I’ve learned tons. Many of my friends have also followed suit and become TAs with the Dept. of Mathematics. I recommend qualified undergraduate students to consider applying for a teaching assistant position.

Summary

I first realized I enjoyed teaching when I taught AP Physics in high school. It brings fulfilment and a sense of meaning to knowledge.
Being able to explain things well to others really tests your own knowledge. Not only do you have to understand the material inside and out, but you have to also understand the areas where students struggle. Being a TA has furthered my own knowledge and communication skills.
Good money.

Misc

Math Aid Centre (MAC) assignment hours are fun and relaxing. Students approach you with questions in a 1-on-1 setting and you’re able to spend time with them to go over the material.
Since I became a TA in second year, it happened often that a classmate in one class was my student in another!
I greatly prefer teaching over marking. Marking is a chore. Teaching is a choice.
Undergraduate TAs in the Physics Dept. are nonexistent because there simply isn’t that much demand.
I have tried to TA with the Math Dept. in the summer but since demand is less, they typically give those assignments to grad students.
Engineering students are more rowdy then ArtSci students.

Courses I have TA’ed

Term	Course
Winter 2018	MAT224H : Linear Algebra II
Fall 2017	MAT245H : Mathematical Methods in Data Science
Winter 2017	MAT224H : Linear Algebra II
Fall 2016 - Winter 2017	MAT237Y : Multivariable Calculus
Fall 2016	MAT188H : Linear Algebra (for Engineers)
Winter 2016	MAT224H : Linear Algebra II
Fall 2015	MAT223H : Linear Algebra I

Book List

2017-03-11T00:00:00+00:00

My Book List is now live! Come take a look at what I’ve read so far.

HackOn(Data) 2016

2017-01-22T00:00:00+00:00

For our third-place project description, click here
For the video of our presentation, click here

Last September, I participated in HackOn(Data), a two-day data hackathon in Toronto. It was the first year it was held and one of the few data science competitions in Toronto. I learned a lot, met similar-minded data enthusiasts and even ended up winning third-place with my teammate Chris!

The Preparation

In the two months leading up to HackOn(Data), I completed weekly online and in-person workshops that built a prerequisite knowledge of Apache Spark (on Databricks), machine learning and general data handling. I really enjoyed these workshops as they guided us through practical problems and techniques involving real datasets. Participants also competed against each other for points, which could be attained through completion of workshops and challenges, and other promotional activities. The top 5 participants received prizes at the HackOn(Data) event, but unfortunately, I finished in seventh place, shy of the prizes.

During the last in-person workshop before HackOn(Data), I met Chris Goldsworthy, a fellow University of Toronto Student in CS, and we decided to form a team. The challenges and datasets for the main HackOn(Data) event were released a week in advance, so Chris and I met up in the days before the event to discuss details. We chose to do the challenge that we thought was the most interesting and open-ended: an unsupervised learning project to determine optimal map placements in Toronto based on datasets of cultural centre locations, transit stops and traffic.

We then brainstormed ideas and created preliminary models for our challenge, which was crucial for us as we quickly eliminated bad or infeasible ideas and set up a concrete workflow. Had we waited until the event, it would have been much more difficult to find our path and complete meaningful work, due to the unsupervised nature of our challenge and the slim 24 hours we had to work on our project during the event. It was at this point that we came up with the idea to augment the existing datasets with social media data mined from Foursquare, a unique feature of our project.

The Event

Saturday morning, I arrived at Wattpad HQ, the event location of HackOn(Data), by foot and late, regretting my decision to walk 3km in new dress shoes. After formalities, presentations and a nutritious lunch, Chris and I locked down a secluded place to work in the upstairs loft, furnished with sofas and a spare mattress. We met with our project mentor, Ryan, a City of Toronto geospatialist expert, to discuss ideas and receive useful feedback. At HackOn(Data), there were an abundance of mentors; at times, it seemed like there were more mentors than participants, which meant that there was always plenty of help available.

Chris and I spent the remainder of the day hacking and snacking until it was time to go home for the night. Unlike traditional hackathons, geared towards sleepless university students, HackOn(Data) was meant more for professionals, and hence closed down for the night, to resume in the morning.

The deadline for the submission was the next morning so Chris and I continued our work back on UofT main campus that night and in the early morning to finish our project and prepare our presentation. We arrived back at Wattpad HQ Sunday morning exhausted but finished and content with our work. After breakfast and more sponsor talks, it was time for the first round of judging.

The Judging

During the initial round of presentations, Chris and I gave a very quick overview of our method and code to a panel of 6 judges and did well enough to progress to the final four round. We had roughly an hour to prepare our final pitch, which was to be given to everyone at HackOn(Data), including a panel of 4 finalist judges.

First Round Judges

We presented third. Our presentation went smoothly but our question period was long and tough, and we received many questions on the methods we used that went beyond our original task, such as mining social media data from Foursquare and a scoring system to rank our final placements (see project page). After some intense grilling, we concluded our presentation and felt a great surge of relief knowing that the most stressful part was over.

Chris and I during our final presentation

After some deliberation by the judges, the results were announced: We won third place! Chris and I were very happy about our result, considering that we were just two undergraduate students, relatively new to data science, competing against industry professionals and PhDs. For our efforts, we received $300 in cash, two Amazon Kindles, and various small gifts.

Receiving Third Place Prizes

The Afterparty

With the awards ceremony, the HackOn(Data) event was over, but the fun was just about to begin. After closing remarks, we went to have drinks and food at a nearby restaurant, courtesy of HackOn(Data). Perhaps the most important part of HackOn(Data), it was here where we chatted with bigwigs from Flipp, Wattpad, IBM and the City of Toronto, as well as graduate students with similar desires to enter the data science industry. We received useful advice about the data science industry, and made important connections that strengthened our professional networks. The amazing part is that Chris and I both received summer internships with HackOn(Data) sponsor companies, whom we met at the event! With third place prizes in our hands and smiles on our faces, we departed the event, ready to begin the school semester the next day.

Conclusion

An exceptional event, well-organized by Mehrdad and his crew, with numerous sponsors and mentors. Would have liked the possibility to work overnight at Wattpad HQ, but understandably, the overhead cost is huge.
In-person and online workshops in the weeks leading up to HackOn(Data) taught me a great deal about Apache Spark and general ML techniques. The points system was an excellent motivator for me to complete the weekly workshops.
Great prizes but perhaps the best rewards were the experience we gained and the connections we made to industry professionals.
Good food, comfortable spaces, great people.

See you at HackOn(Data) 2017!

A Portrait of the Artist as a Young Man by James Joyce

2017-01-09T00:00:00+00:00

Thus ends my 5-month flirtation with the modernist semi-auto-biography — or Künstlerroman, as the elitists say — detailing the artist’s maturation and coming to grips with Ireland and Catholicism.

“When the soul of a man is born in this country there are nets flung at it to hold it back from flight. You talk to me of nationality, language, religion. I shall try to fly by those nets”

It was not a long book but its sheer breadth and abstraction made me pause often for breath when attempting to finish the chapter or page or paragraph. It is because of this that I fear I will never be able to complete Ulysses or Finnegan’s Wake. Portrait was the first Joycean book I attempted to read, not the first that I had completed; I had attempted to read Portrait when I was a mere eleventh grader and I was lost immediately at moocow. I accepted my ineptitude and moved on to other, more palatable works, including Joyce’s Dubliners, an assembly of grounded short stories and vignettes of Dublin life.

Portrait begins with Joyce, a.k.a Stephen Dedalus, as a toddler, moves to him as a precocious boy at Clongowes boarding school, to him in Dublin, to his succumbence to lust, to his instilled fear of the soul in mortal sin, to his devout adherence to religion, to his defiant repudiation of religion, to his emergence as an artist, reborne on the beach, to him among his deserved peers at University College Dublin, to his self-exile from Ireland.

“This was the call of life to his soul not the dull gross voice of the world of duties and despair, not the inhuman voice that had called him to the pale service of the altar.”

Later, the reader achieves glimpses of Dedalus among his peers at University College, where they banter in some intellectual fashion. Dedalus expresses himself on many issues: art, estheticism and religion. In the end, he decides to leave Ireland for Europe, saying:

“I will not serve that in which I no longer believe whether it call itself my home, my fatherland or my church: and I will try to express myself in some mode of life or art as freely as I can and as wholly as I can, using for my defence the only arms I allow myself to use — silence, exile, and cunning.”

He leaves his homeland in search for some expression or freedom as an artist, to escape his stagnant environment. He is willing to be alone, “(n)ot only to be separate from all others but to have not even one friend.” Perhaps one day I will follow suit.

Moments

Dedalus and his beloved on the last tram
Scence on the beach
The Villanelle
Banter at University College

Other Quotes

“His soul had arisen from the grave of boyhood, spurning her graveclothes. Yes! Yes! Yes! He would create proudly out of the freedom and power of his soul, as the great artificer whose name he bore, a living thing, new and soaring and beautiful, impalpable, imperishable.”
“Old father, old artificer, stand me now and ever in good stead.” See Daedalus, the Greek architect. Joyce does not get burned like Icarus.

The Beginning

2017-01-05T00:00:00+00:00

This is the first post on the blog by Danny Luo.

Danny Luo

Thoughts on Nietzsche's Beyond Good and Evil

Self-Analysis

Preface

On the Prejudices on Philosophers

The Free Spirit

Das Religiöse Wesen

Epigrams and Interludes

Natural History of Morals

Morality as Herd Timidity

We Scholars

Our Virtues

On Cruelty and the Spirit

On Woman

Peoples and Fatherlands

What is Noble

Master and Slave Moralities

Beginning of the End

From High Mountains: Aftersong

Concluding Thoughts

References

Running ML Inference Services in Shared Hosting Environments

Abstract

What You’ll Learn

Modern Agile for Machine Learning

Abstract

Toronto Deep Learning Series — Google BERT

Slides

From Academia to Industry

Contents

My Story

Discontent with Academia

Initial Interest in Data Science & Machine Learning

Projects & Competitions

Internship

Industry or Grad School

Job Search

September, October — Light Searching and Reconnaissance

November, December — Applying, Interviewing, Pivoting

January, February — Interviews, People-Oriented Approach

March, April — Negotiation, Decisions

Advice

Personal Projects

Networking

Job Hunting

Start Early

Have Options

People-Oriented Approach

Negotiate

Conclusion

Links

An Honest Look at UofT

What is UofT Like?

You Aren’t Admitted into Your Program of Study Until Second Year

The College System

Tough Marking

Large Class Sizes

Large Commuter Population

Large International Population

Little School Spirit

Bureaucratic

Research

Tips on Success

Parting Thoughts

About Me

Spark Performance Tuning

Machine Learning Resources

Toronto Apache Spark 19

HackOn(Data) 2017

University of Toronto Data Science Team

Multi-CPU Data Processing

Setup

Multiprocessing

Accessing S3 Data in Python with boto3

Setup

Reading and Writing Files

Iterating Over Bucket

Conclusion

My Experience as a TA

Summary