VIDEO | CS224N: The Complete Course in 20 Minutes

The ultimate high-yield visual recap of Stanford’s CS224N (Natural Language Processing with Deep Learning)! In just 20 minutes, we cover the full 15-module arc of the course—from the birth of word vectors to modern reasoning models and the smart scaling era.Key Topics Covered:Word Embeddings & Recurrent Networks: From distributional semantics and Word2Vec to backpropagation, RNNs, and sequence-to-sequence bottlenecks.The Transformer & Pretraining Revolution: Self-attention mechanics, BPE tokenization, BERT, GPT, T5, and Chinchilla scaling laws.Alignment & Efficient Adaptation: Instruction fine-tuning, RLHF, DPO, and low-rank parameter adaptation (LoRA/QLoRA).RAG, Agents & Evaluation: Dense Passage Retrieval, ReAct agents, tool use, and modern model-based evaluation metrics.Reasoning, Multimodality & Smart Scaling: Speculative decoding, DeepSeek-R1 / GRPO, tokenization taxes, interpretability illusions, CLIP, and Prolonged RL (ProRL).Note: This is an AI-generated visual discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.