halberdofpineapple/neural-networks-and-deep-learning

This repository is a collection of working code examples that accompany an online book about neural networks and deep learning. If you're reading the book and want to actually run the code to see how the concepts work in practice, this is where you'll find it. The code demonstrates the fundamental ideas behind neural networks, how computers can learn patterns from data by adjusting internal parameters over time. The examples start simple and build up to more complex techniques. You can run these programs to train networks on real datasets, watch them improve, and understand what's happening at each step. It's the difference between reading "here's how backpropagation works" and actually seeing it happen on your screen. This project is aimed at people learning deep learning from the ground up. If you're working through the book and want hands-on experience, or if you're teaching neural networks and need concrete examples to show students, these code samples provide that. A student might run the code to train a network on handwritten digits, then tweak the settings to see how different choices affect the results. A teacher might use it as a foundation for assignments. One important note: this code was written for older versions of Python (2.6 or 2.7) and an old deep learning library called Theano. The author has stated they won't be updating it for newer versions, the code is frozen as a companion to the book. If you want to run it today, you'd likely need to set up a legacy Python environment or use someone's updated Python 3 version (which the README points to). It's designed as educational material to understand core concepts, not as a library you'd use to build new projects.