Lecture 15 - Training Large Models
This lecture studies techniques to reduce memory consumption and scale up model training.

▶︎
Ultimate Guide To Scaling ML Models - Megatron-LM | ZeRO | DeepSpeed | Mixed Precision

▶︎
Lecture 18 - Sequence Modeling and Recurrent Networks

▶︎
Build and Train Your Own Large Language Model from Scratch with PyTorch

▶︎
23 - Model Deployment

▶︎
Lecture 13 - Hardware Acceleration Implemention

▶︎
Lecture 11 - Hardware Acceleration
![Yann LeCun's $1B Bet Against LLMs [Part 1]](https://i.ytimg.com/vi/kYkIdXwW2AE/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLDbV4izF3i-wxevCVIn7FJjoy1vlA)
▶︎
Yann LeCun's $1B Bet Against LLMs [Part 1]

▶︎
Lecture 17 - Generative Adversarial Networks Implementation

▶︎
Training Sand to Think: Artificial General Intelligence & Future of Physics

▶︎
AlphaFold - The Most Useful Thing AI Has Ever Done

▶︎
Taiwan's DRAM Failure

▶︎
A Brief History of AI: From Machine Learning to Gen AI to Agentic AI

▶︎
How To Think SO CLEARLY People Assume You're A Genius

▶︎
Lecture 7 - Neural Network Abstractions

▶︎
Lecture - 12 GPU Acceleration

▶︎
Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)

▶︎
Lecture 20 - Transformers and Attention

▶︎
Lecture 19 - RNN Implementation

▶︎
RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source

▶︎
