AI 与机器学习karpathy.github.io编辑精选
神经网络黑客指南
Hacker's Guide to Neural Networks
2014 — 2019，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这是 Andrej Karpathy 写的神经网络入门教程，面向程序员，从最简单的表达式求导讲起，逐步推导出反向传播和梯度下降，最后引出神经网络。它以“黑客”式的直觉化讲解代替枯燥数学，让普通开发者也能真正理解神经网络的工作原理。
为什么经久不衰：它用程序员熟悉的直觉化方式拆解了神经网络和反向传播的“魔法”，多年来一直是公认最好的入门材料之一，今天读依然能打下扎实的直觉基础。
HN 评论精选
作者本人说明这是仍在完善的业余作品，并顺带介绍了 CS231n 课程
感谢点赞！不过有点纠结这篇文章被到处转发，因为它还在持续完善中。我利用业余时间写这份指南，是因为对这些模型感兴趣的人很多，但好的讲解太少。另外，下学期我会和导师合教斯坦福的 CS231n 课程（卷积神经网络），希望能把大量材料和代码免费公开。
karpathy查看英文原文
Thanks for the upvotes! I'm a little conflicted about this being linked around too much because it is still very much work in progress. I work on this guide on a side because I think there's a lot of interest in these models out there, and not enough explanations. On a related note, some of you might also be interested in a Stanford CS class I'll be co-teaching with my adviser next quarter. It will be focused primarily on Convolutional Networks (but a lot of the learning machinery is generic): CS231n: Convolutional Neural Networks for Visual Recognition http://vision.stanford.edu/teaching/cs231n/ I hope to make a lot of the materials/code freely available so everyone can follow along, and I will continue my work on this guide in parallel whenever I can squeeze in time. (And I'd be happy to hear any feedback on the content/style/presentation)
普通程序员视角：这篇文章把神经网络的神秘感彻底讲透了
很棒的指南——这是唯一一篇读完后没让我头疼、真正让我理解的材料。我不是数学或机器学习背景，只是个普通程序员，以前只会把神经网络库当黑盒用。读完我的理解是：神经网络本质上就是一个用来猜出另一个公式最优参数的公式，并随着训练数据不断精化这些参数。之前理论上就知道，但现在神秘感彻底消失了。
jaza查看英文原文
Great guide - the only material I've ever read on the subject, that hasn't completely made my head hurt and my brain not grok. I'm not a maths or ML guy, just a regular programmer. I've dabbled in NN before, but only to the extent of using some libraries as a "black box" to pass parameters / training data to. No doubt there are many more people like me (too many!). My understanding after reading this guide, is that a neural network is essentially just a formula for guessing the ideal parameter(s) of another formula, and for successively refining those parameters as more training data is passed in. I already knew this "in theory" before, but now I think most of the magic and mystery has been brushed off. Thanks karpathy!
赞其讲解口吻像当面授课，每一步都照顾读者的思路
这是第一个真正让我开窍的神经网络讲解，因为它的叙述口吻就像作者当面给你讲一样。大学讲课之所以有效，是因为假设你具备全部前置知识、每一步都能跟上、且不超出注意力时长。很多文章的作者懒得预判读者的思考过程，逼得读者去别处查答案，写作的冲击力就全稀释了。
zackmorris查看英文原文
This is the first explanation of neural nets that has really clicked for me. That's because it's written in the same explanatory tone that I would use in person while trying to convey the concepts. I wish more people would write like this. I think of these things like lectures in college. Assuming that a) you have all the context up to that point, b) you grok every step, and c) it doesn't run beyond the length of you attention span - call it an hour - then you can learn astonishing things in a matter of a few sessions. But if you miss a step, and don't raise your hand because you think your question is stupid, then you might as well pack up and go home. I find that even though the time element of articles is removed, I still trip over certain sections because the author was too lazy to anticipate my thinking process, which forces me to search other sites for the answers, and before you know it the impact of the writing has been completely diluted.
提醒：这类直觉化指南只能入门，扎实的数学基础才是根本
想真正理解神经网络、机器学习和大部分 AI，首先应该花时间学好概率论和多元微积分。这类黑客指南只是浅尝辄止，某种程度上也助长了这个领域“黑魔法”的光环；不是说这不好，但它应该是辅助材料，而不是正道。
postit查看英文原文
A good sit in probability theory and multivariate calculus is the first thing you should spend your time if you want to understand NN, ML and most of AI for once. These hacker guides only scratch the surface of the subject which, in part, contributes to creating this aura of black magic that haunts the field; I'm not saying that is a bad thing though, but it needs to be a complementary material, not the way to go.
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