Learn Basic of AI

This video, presented by Andrej Karpathy, provides a comprehensive, hands-on tutorial on building a Generatively Pretrained Transformer (GPT) from scratch using Python and PyTorch. The lecture demystifies the technology behind systems like ChatGPT by walking through the implementation of a decoder-only Transformer architecture. Key Highlights of the Tutorial: Foundations of Language Modeling (0:07:52 - 0:34:53): The process begins with setting up the environment, exploring the Tiny Shakespeare dataset, and implementing a simple bigram language model as a baseline. Building Self-Attention (0:42:13 - 1:19:11): This is the core of the video, where the viewer learns how tokens communicate. It progresses from simple averaging (bag of words) to scaled self-attention using matrices, explaining how queries, keys, and values enable data-dependent interactions. Constructing the Transformer (1:19:11 - 1:42:39): The model is scaled up by implementing multi-headed attention, feedforward layers, residual connections, and layer normalization to stabilize training. Context and Theory (1:42:39 - 1:56:20): Karpathy discusses the difference between encoder and decoder blocks, briefly walks through the nanoGPT codebase, and explains the two-stage process (pre-training followed by fine-tuning/RLHF) required to create a production-ready assistant like ChatGPT.