deep-learning-book

Why we included this project

This is the companion repository to Sebastian Raschka's practical deep learning book, organized around Jupyter notebooks and PyTorch code rather than a deployable library. It moves through the field in a deliberate order, from the perceptron and gradient descent up through convolutional and recurrent networks, autoencoders, GANs, and reinforcement learning. If you already know Python and the basics of machine learning and want to get comfortable building and tuning neural networks, the worked examples and chapter-end exercises give you a structured path that scattered blog posts rarely provide. The chapters build on each other, so it works best as a course to follow sequentially, with the notebooks handy as a reference once the ideas sink in.