Difference between revisions of "AI tutorials"
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==Diffusion== | ==Diffusion== | ||
* 2024-06: [https://arxiv.org/abs/2406.08929 Step-by-Step Diffusion: An Elementary Tutorial] | * 2024-06: [https://arxiv.org/abs/2406.08929 Step-by-Step Diffusion: An Elementary Tutorial] | ||
| + | * 2025-04: [https://sander.ai/2025/04/15/latents.html Generative modelling in latent space] | ||
==Transformer== | ==Transformer== | ||
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* [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] | ||
| + | * [https://github.com/apple/embedding-atlas?tab=readme-ov-file Embedding Atlas] ([https://apple.github.io/embedding-atlas/ demo]) | ||
Latest revision as of 11:08, 15 August 2025
Contents
General
- MLU-EXPLAIN
- Deep Learning is Not So Mysterious or Different
- AI Digest: Interactive AI explainers
- OpenAI Academy
- Evolution of LLMs
Loss Functions
Diffusion
- 2024-06: Step-by-Step Diffusion: An Elementary Tutorial
- 2025-04: Generative modelling in latent space
Transformer
- Low-level:
- 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
- Understanding Positional Encoding in Transformers
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
