VIDEO | CS224N: The Complete Course in 20 Minutes

<p>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.</p><p><strong>Key Topics Covered:</strong></p><ul><li><p><strong>Word Embeddings &amp; Recurrent Networks:</strong> From distributional semantics and Word2Vec to backpropagation, RNNs, and sequence-to-sequence bottlenecks.</p></li><li><p><strong>The Transformer &amp; Pretraining Revolution:</strong> Self-attention mechanics, BPE tokenization, BERT, GPT, T5, and Chinchilla scaling laws.</p></li><li><p><strong>Alignment &amp; Efficient Adaptation:</strong> Instruction fine-tuning, RLHF, DPO, and low-rank parameter adaptation (LoRA/QLoRA).</p></li><li><p><strong>RAG, Agents &amp; Evaluation:</strong> Dense Passage Retrieval, ReAct agents, tool use, and modern model-based evaluation metrics.</p></li><li><p><strong>Reasoning, Multimodality &amp; Smart Scaling:</strong> Speculative decoding, DeepSeek-R1 / GRPO, tokenization taxes, interpretability illusions, CLIP, and Prolonged RL (ProRL).</p></li></ul><p><strong>Note:</strong> This is an AI-generated visual discussion created using Google&#39;s NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.</p>