Lecture 3 decomposes neural-network training into computation graphs, local derivatives, and the chain rule: the forward pass computes a result; backprop accumulates gradients from the output so every parameter knows how to move.
Lecture 11 divides evaluation into what to test, how to measure it, and when the result stops being trustworthy. Benchmarks saturate or leak, prompts change scores, and an LLM judge remains a biased model.
Lecture 9 compares prompting, pruning, LoRA, prompt tuning, and adapters. Each asks the same question: how many parameters must change, and how much task-specific state must be stored, to adapt a large pretrained model?
Lecture 6 completes the Transformer picture with encoders, decoders, and cross-attention, then breaks the final project into formats, assessment, research topics, and data. A viable topic needs one explicit baseline and metric.
Winter 2026 Lecture 1 divides NLP into four eras: early exploration, symbolic systems, statistical machine learning, and deep/self-supervised learning. The point is not the dates but how each era redefined the language problem.
Lecture 15 is Been Kim's interpretability guest session, but the Winter 2026 site publishes no slides or agenda. This article does not invent lecture content; it maps the five official readings across concept discovery, agentic investigation, and new vocabulary.
Lecture 17 is Luke Zettlemoyer's multimodality guest session, but the site publishes no slides or agenda. Its official readings establish three routes: visual reasoning workspaces, early-fusion token models, and text autoregression with image diffusion.
The final lecture frames Open Questions in NLP 2026 as smart scaling: prolonged RL, Prismatic synthetic data, RL as pretraining, and open collaboration seek reasoning gains beyond adding parameters.
Lecture 8 explains how instruction tuning, preference data, and RLHF turn a pretrained model into an assistant, then derives DPO from winner–loser pairs. Every step converts human judgment into signal—and imports its biases.
Lecture 7 decomposes pretraining into scalable data, subword tokenization, three model objectives, and in-context learning. A general self-supervised objective yields reusable representations; downstream signals specify their use.
Lecture 10 moves from question answering and RAG into language agents, then decomposes them into reasoning and planning, memory, tools, data, and evaluation. An agent is an inspectable loop between a model and external state.
Lecture 12 shows that output policy is not a detail: greedy, beam, and sampling produce different text. It then moves from R1-Zero/R1 into PPO, GRPO, and DAPO, asking when longer reasoning actually helps.
Lecture 13 moves from inference efficiency to inference capability: speculative decoding drafts with a small model and verifies with a large one; on-policy distillation addresses drift; long context and test-time scaling spend inference resources.
Lecture 4 defines a language model as a next-word probability distribution, then uses an RNN to compress an arbitrarily long prefix. It also exposes recurrence's central cost: information and gradients travel one time step at a time.
Lecture 16 divides NLP's social impact into four questions: why models hallucinate, why AI-assisted creativity may homogenize output, how work is reorganized, and why value alignment cannot be reduced to one reward.
Work through a low-rank update, distinguish update rank from full-weight rank, and count trainable parameters. This preparation follows the LoRA paper and does not reconstruct the unavailable guest session.
Lecture 14 moves from word, character/byte, and subword segmentation to BPE failures and cross-lingual fairness. A tokenizer determines sequence length, compute cost, and the units a model sees; it is not neutral preprocessing.
Lecture 5 moves from the long-range and sequential bottlenecks of RNNs to self-attention and the Transformer. It shortens information paths and enables parallel computation, at the price of quadratic attention and separately encoded position.
Lecture 2 moves from word2vec's prediction task, objective, and gradients to count-based vectors and evaluation. Meaning becomes a high-dimensional position learned from context, not a label retrieved from a dictionary.
CS224N is Stanford's course on natural language processing with deep learning: it goes from word vectors and backpropagation to Transformers, every later lecture builds on Transformers, and the four assignments include writing a decoder-only Transformer yourself. The current slides and assignments are public, and Assignment 3 ships unit tests that run on a laptop; the complete public recordings come from an earlier offering and no longer match the current syllabus.