We take the 2-layer MLP from previous video and make it deeper with a tree-like structure, arriving at a convolutional neural network architecture similar to the WaveNet (2016) from DeepMind. In the WaveNet paper, the same hierarchical architecture is implemented more efficiently using causal dilated convolutions (not yet covered). Along the way we get a better sense of torch.nn and what it is and how it works under the hood, and what a typical deep learning development process looks like (a lot of reading of documentation, keeping track of multidimensional tensor shapes, moving between jupyter notebooks and repository code, ...).

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_part5_cnn1.ipynb
- collab notebook: https://colab.research.google.com/drive/1CXVEmCO_7r7WYZGb5qnjfyxTvQa13g5X?usp=sharing
- my website: https://karpathy.ai
- my twitter: https://twitter.com/karpathy
- our Discord channel: https://discord.gg/3zy8kqD9Cp

Supplementary links:
- WaveNet 2016 from DeepMind https://arxiv.org/abs/1609.03499
- Bengio et al. 2003 MLP LM https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

Chapters:
intro
00:00:00 intro
00:01:40 starter code walkthrough
00:06:56 let’s fix the learning rate plot
00:09:16 pytorchifying our code: layers, containers, torch.nn, fun bugs
implementing wavenet
00:17:11 overview: WaveNet
00:19:33 dataset bump the context size to 8
00:19:55 re-running baseline code on block_size 8
00:21:36 implementing WaveNet
00:37:41 training the WaveNet: first pass
00:38:50 fixing batchnorm1d bug
00:45:21 re-training WaveNet with bug fix
00:46:07 scaling up our WaveNet
conclusions
00:46:58 experimental harness
00:47:44 WaveNet but with “dilated causal convolutions”
00:51:34 torch.nn
00:52:28 the development process of building deep neural nets
00:54:17 going forward
00:55:26 improve on my loss! how far can we improve a WaveNet on this data?

(From Youtube)