Build and train LLM NLP transformers and attention mechanisms (PyTorch). Explore with mechanistic interpretability tools
InstructorMike X Cohen
Free
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the major course topics, covering technical areas such as tokenization, embeddings vectors, LLM architecture, pretraining, evaluations, and AI safety.
This slide outlines the lecture types including video explanations, Python demos, exercises, and links to freely available code on GitHub.
This slide outlines the ideal background for studying large language models, noting that while a desire to learn is necessary, knowledge of Python and machine learning is helpful.
The curriculum provides depth in both non-causal observation of embeddings and causal intervention techniques such as head ablation and MLP removal.
Technical modules utilize a recurring 'CodeChallenge' format to reinforce concepts like tokenization and transformer implementation.
The structure includes dedicated tracks for Python programming and the mathematics of deep learning, including gradient descent and backpropagation.
The volume of material is substantial; one signal from early 2026 suggests the content could be divided into multiple smaller courses. Note that this review predates the displayed June 2026 update, and the update label does not prove a correction to this density.
The curriculum aligns well with the requirements for math and programming foundations needed to navigate the advanced topics.