t81_558_deep_learning

Why we included this project

Jeff Heaton's graduate course at Washington University in St. Louis ships with everything a self-taught learner needs: the full textbook lives in the repo, along with lecture notebooks and Python coding assignments built on TensorFlow and Keras. The material covers convolutional and recurrent networks, GANs, and reinforcement learning, with hands-on examples for tabular data, computer vision, time series, NLP, and data generation. That makes it a practical curriculum for anyone who wants to move from the basics to real applications by writing code, not just reading theory. One thing to know: the university now teaches a PyTorch edition of the same course, so this Keras version functions as an archive of proven teaching material rather than an actively maintained codebase. Even so, the notebooks are clear enough to serve as reference implementations for developers who want to refresh their deep learning fundamentals.