Build each major neural architecture yourself
A guided tour of the field's main neural architectures in which you implement each one rather than just read about it — plain networks first, then convolutional models for vision, sequence models and Transformers for language, and finally generative models that synthesise new images.
GANs and diffusion models
Where an introductory course explains architectures, this one makes you implement them. You move from the feedforward basics into convolutional networks for computer vision, then sequence models and Transformers, and close on generative models — the GANs and diffusion systems behind synthetic imagery. It sits squarely inside deep learning, a deliberate step beyond general machine learning.
The program's central subject, end to end.
Builds and trains networks from the ground up.
A full module on sequence models and Transformers.
Convolutional networks for image tasks.
Builds GANs and diffusion models.
Sequence models applied to language.
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