This video provides a comprehensive, code-based tutorial on building a GPT-like language model from scratch, focusing on the Transformer architecture. It covers fundamental AI/ML concepts such as tokenization, positional embeddings, self-attention, multi-head attention, residual connections, and layer normalization, demonstrating their implementation in PyTorch. The tutorial culminates in scaling up a character-level model trained on Shakespeare, and concludes by contrasting the implemented decoder-only Transformer with the encoder-decoder architecture and outlining the multi-stage training process of large language models like ChatGPT, including pre-training and reinforcement learning-based fine-tuning.