Difference between revisions of "AI tutorials"
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==General== | ==General== | ||
* [https://mlu-explain.github.io/ MLU-EXPLAIN] | * [https://mlu-explain.github.io/ MLU-EXPLAIN] | ||
| + | * [https://arxiv.org/abs/2503.02113 Deep Learning is Not So Mysterious or Different] | ||
| + | * [https://theaidigest.org/ AI Digest]: Interactive AI explainers | ||
| + | * [https://academy.openai.com/ OpenAI Academy] | ||
==Loss Functions== | ==Loss Functions== | ||
| Line 16: | Line 19: | ||
==LLM== | ==LLM== | ||
| + | * [https://transformer-circuits.pub/2022/toy_model/index.html Toy Models of Superposition] | ||
| + | * [https://www.techrxiv.org/doi/full/10.36227/techrxiv.23589741.v1 A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage] | ||
* [https://aman.ai/ Aman AI]: [https://aman.ai/primers/ai/LLM/ Overview of Large Language Models] | * [https://aman.ai/ Aman AI]: [https://aman.ai/primers/ai/LLM/ Overview of Large Language Models] | ||
| + | * [https://github.com/Hannibal046/Awesome-LLM Awesome-LLM]: Curated list of LLM projects | ||
| + | * [https://book.theaiedge.io/ The Big Book of Large Language Models] (Damien Benveniste) | ||
| + | * 2025-01: [https://arxiv.org/abs/2501.09223 Foundations of Large Language Models] | ||
| + | |||
| + | ===Video=== | ||
| + | * Andrej Karpathy: | ||
| + | ** [https://www.youtube.com/watch?v=EWvNQjAaOHw How I use LLMs] | ||
| + | ** [https://www.youtube.com/watch?v=7xTGNNLPyMI Deep Dive into LLMs like ChatGPT] | ||
| + | |||
| + | ===Prompt Engineering=== | ||
| + | * 2025-04: Lee Boonstra (Google): [https://www.kaggle.com/whitepaper-prompt-engineering Prompt Engineering] | ||
==Other Visualizations== | ==Other Visualizations== | ||
| + | * [https://distill.pub/2019/visual-exploration-gaussian-processes/ A Visual Exploration of Gaussian Processes] | ||
* [https://pytorch.org/blog/inside-the-matrix/ Inside the Matrix: Visualizing Matrix Multiplication, Attention and Beyond] | * [https://pytorch.org/blog/inside-the-matrix/ Inside the Matrix: Visualizing Matrix Multiplication, Attention and Beyond] | ||
* [https://sohl-dickstein.github.io/2024/02/12/fractal.html Neural network training makes beautiful fractals] | * [https://sohl-dickstein.github.io/2024/02/12/fractal.html Neural network training makes beautiful fractals] | ||
Latest revision as of 13:54, 13 April 2025
Contents
General
- MLU-EXPLAIN
- Deep Learning is Not So Mysterious or Different
- AI Digest: Interactive AI explainers
- OpenAI Academy
Loss Functions
Transformer
- Wolfram: What Is ChatGPT Doing … and Why Does It Work?
- The Illustrated Transformer
- Transformers Explained Visually — Not Just How, but Why They Work So Well
Visualizations
LLM
- Toy Models of Superposition
- A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage
- Aman AI: Overview of Large Language Models
- Awesome-LLM: Curated list of LLM projects
- The Big Book of Large Language Models (Damien Benveniste)
- 2025-01: Foundations of Large Language Models
Video
- Andrej Karpathy:
Prompt Engineering
- 2025-04: Lee Boonstra (Google): Prompt Engineering
