Andrej Karpathy | Building makemore Part 5: Building a WaveNet @AndrejKarpathy | Uploaded 1 year ago | Updated 2 hours ago
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: github.com/karpathy/makemore
- jupyter notebook I built in this video: github.com/karpathy/nn-zero-to-hero/blob/master/lectures/makemore/makemore_part5_cnn1.ipynb
- collab notebook: colab.research.google.com/drive/1CXVEmCO_7r7WYZGb5qnjfyxTvQa13g5X?usp=sharing
- my website: karpathy.ai
- my twitter: twitter.com/karpathy
- our Discord channel: discord.gg/3zy8kqD9Cp
Supplementary links:
- WaveNet 2016 from DeepMind arxiv.org/abs/1609.03499
- Bengio et al. 2003 MLP LM 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?
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: github.com/karpathy/makemore
- jupyter notebook I built in this video: github.com/karpathy/nn-zero-to-hero/blob/master/lectures/makemore/makemore_part5_cnn1.ipynb
- collab notebook: colab.research.google.com/drive/1CXVEmCO_7r7WYZGb5qnjfyxTvQa13g5X?usp=sharing
- my website: karpathy.ai
- my twitter: twitter.com/karpathy
- our Discord channel: discord.gg/3zy8kqD9Cp
Supplementary links:
- WaveNet 2016 from DeepMind arxiv.org/abs/1609.03499
- Bengio et al. 2003 MLP LM 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?
![[1hr Talk] Intro to Large Language Models](https://i.ytimg.com/vi/zjkBMFhNj_g/hqdefault.jpg "[1hr Talk] Intro to Large Language Models
This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm.
As of November 2023 (this field moves fast!).
Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, thats my hotel room during the thanksgiving break.
- Slides as PDF: https://drive.google.com/file/d/1pxx_ZI7O-Nwl7ZLNk5hI3WzAsTLwvNU7/view?usp=share_link (42MB)
- Slides. as Keynote: https://drive.google.com/file/d/1FPUpFMiCkMRKPFjhi9MAhby68MHVqe8u/view?usp=share_link (140MB)
Few things I wish I said (Ill add items here as they come up):
- The dreams and hallucinations do not get fixed with finetuning. Finetuning just directs the dreams into helpful assistant dreams. Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the working memory of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, Id always recommend double-checking the math/code the LLM did.
- How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code above that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the “system message”).
- You might also enjoy my 2015 blog post Unreasonable Effectiveness of Recurrent Neural Networks. The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/llama2.c/blob/master/run.c
Chapters:
Part 1: LLMs
00:00:00 Intro: Large Language Model (LLM) talk
00:00:20 LLM Inference
00:04:17 LLM Training
00:08:58 LLM dreams
00:11:22 How do they work?
00:14:14 Finetuning into an Assistant
00:17:52 Summary so far
00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard
Part 2: Future of LLMs
00:25:43 LLM Scaling Laws
00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E)
00:33:32 Multimodality (Vision, Audio)
00:35:00 Thinking, System 1/2
00:38:02 Self-improvement, LLM AlphaGo
00:40:45 LLM Customization, GPTs store
00:42:15 LLM OS
Part 3: LLM Security
00:45:43 LLM Security Intro
00:46:14 Jailbreaks
00:51:30 Prompt Injection
00:56:23 Data poisoning
00:58:37 LLM Security conclusions
End
00:59:23 Outro")
