Prerequisites
The course assumes a prior machine-learning course and comfort with the mathematics and Python below. These pages support self-assessment and a refresher before Week 1.
Deep learning is applied linear algebra and calculus with a probabilistic flavor. A mathematician's depth is not required, but these ideas should feel familiar so the course can move quickly from notation to networks.
PyTorch is a Python library, and idiomatic Python makes deep-learning code short and readable. Beyond the basics, a handful of features appear constantly in PyTorch and data code; know them and the framework stops feeling mysterious.
This course assumes an introductory machine-learning course. Deep learning reuses its vocabulary, models, losses, splits, and the overfitting story, so the network material lands on familiar ground.