Machine-Learning

A dense, correct reference covering tensors, GPU acceleration, autograd, backpropagation, and training loops. Everything you need to understand how PyTorch trains models.

A dense walkthrough of how large language models work – from next-token prediction to tokenization, embeddings, self-attention with causal masking, multi-head attention, and the full transformer architecture. Based on Andrej Karpathy’s teaching approach.