ML

Tips from my friend Artem on learning ML:

1. Practical Deep Learning for Coders - Practical Deep Learning – course from Jeremy Howard
2. Neural Networks: Zero To Hero – materials by Andrej Karpathy

LLM

LLM models are measured by the number of parameters (e.g. 70B) and speed (e.g. 50 tokens/s for ChatGPT and 300 tokens/s on Groq).

Token generation is a purely serial operation, every single token generated depends on knowing the previous token. There is no parallelism.

• LLM Visualization
• GitHub - karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.
• LMSys Chatbot Arena Leaderboard - a Hugging Face Space by lmsys

Other

• Machine Learning Compilation – tutorial on ML compilation
• Compiling ML models to C for fun | Max Bernstein
• Animated AI
• The Hundred-Page Machine Learning Book by Andriy Burkov
• https://github.com/karpathy/micrograd
• 3Blue1Brown – Neural Networks