Andrej Karpathy
Deep learning taught from first principles.
- 视频数
- 17
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- 177万
- 总观看次数
- 3662.3万
- 最新上传
- 2025年2月27日
- 独立页面
- 机器学习
- 内容语言: 英语
- 地区: 美国
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Deep learning taught from first principles.
这是 Youtop 运营的独立目录页面，并非由 Andrej Karpathy 或 YouTube 运营、赞助或认可。 YouTube 官方频道 ↗提交更正你是这位创作者吗？认领此页面
Andrej Karpathy · 发布于 2022年10月11日
We take the 2-layer MLP (with BatchNorm) from the previous video and backpropagate through it manually without using PyTorch autograd's loss.backward(): through the cross entropy loss, 2nd linear layer, tanh, batchnorm, 1st linear layer, and the embedding table. Along the way, we get a strong intuitive understanding about how gradients flow backwards through the compute graph and on the level of efficient Tensors, not just individual scalars like in micrograd. This helps build competence and intuition around how neural nets are optimized and sets you up to more confidently innovate on and debug modern neural networks. !!!!!!!!!!!! I recommend you work through the exercise yourself but work with it in tandem and whenever you are stuck unpause the video and see me give away the answer. This video is not super intended to be simply watched. The exercise is here: https://colab.research.google.com/drive/1WV2oi2fh9XXyldh02wupFQX0wh5ZC-z-?usp=sharing !!!!!!!!!!!! Links: - makemore on github: https://github.com/karpathy/makemore - jupyter notebook I built in this video: https://github.com/karpathy/nn-zero-to-hero/blob/master/lectures/makemore/makemore_part4_backprop.ipynb - collab notebook: https://colab.research.google.com/drive/1WV2oi2fh9XXyldh02wupFQX0wh5ZC-z-?usp=sharing - my website: https://karpathy.ai - my twitter: https://twitter.com/karpathy - our Discord channel: https://discord.gg/3zy8kqD9Cp Supplementary links: - Yes you should understand backprop: https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b - BatchNorm paper: https://arxiv.org/abs/1502.03167 - Bessel’s Correction: http://math.oxford.emory.edu/site/math117/besselCorrection/ - Bengio et al. 2003 MLP LM https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf Chapters: 00:00:00 intro: why you should care & fun history 00:07:26 starter code 00:13:01 exercise 1: backproping the atomic compute graph 01:05:17 brief digression: bessel’s correction in batchnorm 01:26:31 exercise 2: cross entropy loss backward pass 01:36:37 exercise 3: batch norm layer backward pass 01:50:02 exercise 4: putting it all together 01:54:24 outro
