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“In practice, experienced machine-learning engineers and researchers build intuition over time as to what works and what doesn’t when it comes to these choices—they develop hyperparameter-tuning skills. But there are no formal rules. If you want to get to the very limit of what can be achieved on a given task, you can’t be content with arbitrary choices made by a fallible human. Your initial decisions are almost always suboptimal, even if you have good intuition. You can refine your choices by tweaking them by hand and retraining the model repeatedly—that’s what machine-learning engineers and researchers spend most of their time doing. But it shouldn’t be your job as a human to fiddle with hyperparameters all day—that is better left to a machine.”
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“I shall be perfectly frank with you,” which is how politicians in both Delhi and Washington preface a real whopper of a lie:”
― The Blood Telegram
― The Blood Telegram
“While Pakistan plunged into civil war, Kissinger looked for massacres committed by Bengalis, to generate a moral equivalence that would exonerate Yahya. It would be convenient for Nixon and Kissinger to be able to say that both sides were equally rotten.”
― The Blood Telegram: Nixon, Kissinger, and a Forgotten Genocide
― The Blood Telegram: Nixon, Kissinger, and a Forgotten Genocide
“Some readers are bound to want to take the techniques we’ve introduced here and try them on the problem of forecasting the future price of securities on the stock market (or currency exchange rates, and so on). Markets have very different statistical characteristics than natural phenomena such as weather patterns. Trying to use machine learning to beat markets, when you only have access to publicly available data, is a difficult endeavor, and you’re likely to waste your time and resources with nothing to show for it.
Always remember that when it comes to markets, past performance is not a good predictor of future returns—looking in the rear-view mirror is a bad way to drive. Machine learning, on the other hand, is applicable to datasets where the past is a good predictor of the future.”
― Deep Learning with Python
Always remember that when it comes to markets, past performance is not a good predictor of future returns—looking in the rear-view mirror is a bad way to drive. Machine learning, on the other hand, is applicable to datasets where the past is a good predictor of the future.”
― Deep Learning with Python
“Sometimes fate is like a small sandstorm that keeps changing directions. You change direction but the sandstorm chases you. You turn again, but the storm adjusts. Over and over you play this out, like some ominous dance with death just before dawn. Why? Because this storm isn't something that blew in from far away, something that has nothing to do with you. This storm is you. Something inside of you. So all you can do is give in to it, step right inside the storm, closing your eyes and plugging up your ears so the sand doesn't get in, and walk through it, step by step. There's no sun there, no moon, no direction, no sense of time. Just fine white sand swirling up into the sky like pulverized bones. That's the kind of sandstorm you need to imagine.
And you really will have to make it through that violent, metaphysical, symbolic storm. No matter how metaphysical or symbolic it might be, make no mistake about it: it will cut through flesh like a thousand razor blades. People will bleed there, and you will bleed too. Hot, red blood. You'll catch that blood in your hands, your own blood and the blood of others.
And once the storm is over you won't remember how you made it through, how you managed to survive. You won't even be sure, in fact, whether the storm is really over. But one thing is certain. When you come out of the storm you won't be the same person who walked in. That's what this storm's all about.”
― Kafka on the Shore
And you really will have to make it through that violent, metaphysical, symbolic storm. No matter how metaphysical or symbolic it might be, make no mistake about it: it will cut through flesh like a thousand razor blades. People will bleed there, and you will bleed too. Hot, red blood. You'll catch that blood in your hands, your own blood and the blood of others.
And once the storm is over you won't remember how you made it through, how you managed to survive. You won't even be sure, in fact, whether the storm is really over. But one thing is certain. When you come out of the storm you won't be the same person who walked in. That's what this storm's all about.”
― Kafka on the Shore
Aneesh ’s 2025 Year in Books
Take a look at Aneesh ’s Year in Books, including some fun facts about their reading.
62 books
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402 friends
328 books
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64 friends
36 books
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125 friends
117 books
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38 friends
56 books
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86 friends
96 books
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43 friends
409 books
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89 friends
190 books
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13 friends
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