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Stanford CS224N 2024 — NLP with Transformers Study Pack
Use this as a lecture map before watching, a review guide between classes, or a timestamped index when you need to revisit one concept. CS224N is taught by Professor Christopher Manning at Stanford.
Interactive Stanford CS224N 2024 study pack
CS224N covers word vectors, neural network foundations, convolutional networks for text, and the fundamentals of natural language processing.
Word2Vec introduces distributed representations, skip-grams, and the intuition that similar words appear in similar contexts.
GloVe and evaluation methods cover global word vectors, analogy tests, and intrinsic versus extrinsic evaluation.
Neural network basics addresses computation graphs, loss functions, and the gradient computation that underlies all NLP neural models.
Constituency parsing introduces syntactic structure, context-free grammars, and chart parsing algorithms.
Recurrent neural networks for NLP covers sequence modeling, language modeling, and the challenges of variable-length inputs.
Sequence-to-sequence models introduces encoders, decoders, attention, and the architecture that revolutionized machine translation.
Transformers covers self-attention, positional encodings, and the full encoder-decoder structure behind modern language models.
Pretraining discusses self-supervised learning objectives, BERT's masked language model, and GPT's next-token prediction.
Fine-tuning covers adapting pretrained models, adapter methods, and the practical workflow for transfer learning in NLP.
Question answering addresses extractive QA, span-based models, reading comprehension datasets, and the BiDAF architecture.
Natural language generation covers decoding strategies, nucleus sampling, beam search, and the challenges of coherent long-form text generation.
Summarization explores abstractive versus extractive approaches, pointer-generator networks, and evaluation metrics like ROUGE.
Multilinguality addresses cross-lingual transfer, mBART, XLM-R, and building NLP systems for low-resource languages.
RLHF and alignment introduces reinforcement learning from human feedback, reward modeling, and techniques for aligning model behavior.
Model analysis and reasoning covers chain-of-thought prompting, chain-of-thought fine-tuning, and what language models can and cannot reason about.
Retrieval-augmented generation combines dense retrieval with generation, covering DPR, REALM, and Atlas for knowledge-intensive tasks.
Future directions addresses scaling laws, emergent capabilities, and open problems in language model research.
What this lesson teaches
CS224N is Stanford's introduction to natural language processing with deep learning, taught by Christopher Manning. The course moves from word vector representations through classical NLP, then covers RNNs and sequence-to-sequence models before pivoting to the transformer architecture that now dominates the field. The second half of the course covers pretraining, fine-tuning, question answering, text generation, multilingual models, RLHF, and cutting-edge topics like retrieval-augmented generation and reasoning.
Key concepts and takeaways
- Word vectors represent words as dense vectors where semantic similarity corresponds to geometric proximity in the embedding space.
- Skip-gram and CBOW objectives learn word representations by predicting contexts, producing embeddings that capture relational structure.
- Neural networks for NLP use backpropagation through computation graphs to jointly learn representations and task-specific parameters.
- RNNs process variable-length sequences by maintaining a hidden state updated at each timestep, but struggle with long-range dependencies.
- Attention allows decoders to weight encoder hidden states by relevance, solving the bottleneck problem in sequence-to-sequence models.
- Transformers replace recurrence with self-attention, enabling parallel training and capturing dependencies without sequential computation.
- Pretraining on large unlabeled corpora with self-supervised objectives produces rich representations that transfer to downstream tasks.
- Extractive question answering identifies answer spans in a passage by predicting start and end positions conditioned on the question.
- RLHF uses a learned reward model to fine-tune language models toward human preferences, enabling behavior alignment.
- Retrieval-augmented generation supplements language model generation with relevant retrieved documents, improving factual accuracy.
Review checklist
Use Word2Vec or GloVe embeddings to find semantically similar words and examine what arithmetic relations the vectors capture.
Implement a chart parser for a small context-free grammar and parse a sample sentence.
Train a character-level language model and sample novel sequences to observe what the model has learned.
Implement attention for machine translation and visualize the alignment weights to see which source words the decoder attends to.
Fine-tune a pretrained BERT model on a small text classification dataset and evaluate with and without the pretrained weights.
Build a pointer-generator network for summarization and compare its outputs to a baseline seq2seq model.
Experiment with chain-of-thought prompting on arithmetic and reasoning tasks and measure the improvement over direct prompting.
Lesson chapters
Important source moments
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