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Oxford Deep Learning for NLP — 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. Oxford Deep Learning for NLP is taught by Phil Blunsom at Oxford.
Interactive Oxford NLP study pack
Oxford Deep Learning for NLP covers word vectors, language models, RNNs, self-attention, transformers, and the foundations of modern NLP.
Distributed word representations learn semantic meaning from co-occurrence patterns across large text corpora.
Language models estimate the probability of word sequences, serving as both generative models and rich feature extractors.
RNNs and LSTMs process sequences by maintaining hidden state, but struggle with long-range dependencies without attention.
Self-attention and transformers replace recurrence with learned pairwise interactions between all positions.
Pretrained language models like BERT and GPT apply the transformer architecture to self-supervised language objectives.
What this lesson teaches
Oxford's Deep Learning for NLP course by Phil Blunsom provides a rigorous academic treatment of natural language processing with deep learning. The course moves from distributional semantics through classical NLP to the transformer revolution, covering the mathematical and algorithmic foundations that underlie modern NLP systems. The emphasis is on understanding both why each architecture works and what its limitations are.
Key concepts and takeaways
- Word vectors represent words as dense vectors where geometric distance corresponds to semantic similarity — the distributional hypothesis in algebraic form.
- A language model assigns probabilities to sequences; GPT and BERT learn language models through self-supervised pretraining on large corpora.
- RNNs process sequences of arbitrary length by applying the same transformation at each step, with hidden state carrying information across steps.
- LSTM gating mechanisms allow gradients to flow across longer sequences, mitigating the vanishing gradient problem in vanilla RNNs.
- Self-attention computes a weighted sum of all positions in a sequence, enabling each position to attend to all others in a single operation.
- BERT uses masked language modeling — predicting randomly masked tokens from bidirectional context — producing rich contextual representations.
Review checklist
Train a Word2Vec skip-gram model on a corpus and use the resulting embeddings to find semantically similar words and analogical relationships.
Implement a character-level RNN language model and generate text, analyzing the types of errors the model makes.
Implement scaled dot-product attention from scratch and verify it produces reasonable attention weights on a simple sequence task.
Fine-tune a pretrained BERT model on a small labeled dataset for text classification and evaluate the effect of model size.
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