mrgloom/neural-networks-and-deep-learning

This repository is a collection of Python code examples designed to teach how neural networks and deep learning work. If you're reading a book on these topics, you'll find the actual code here that you can run, study, and experiment with on your own computer. Neural networks are a type of artificial intelligence inspired by how brains work, they learn patterns from data by adjusting internal settings (called "weights") over time. Deep learning refers to networks with many layers, which can recognize complex patterns like faces in photos or meaning in text. Rather than just explaining these concepts in words, this repository gives you working code so you can see them in action. The code is intentionally written to match the book's lessons step-by-step. As you read each chapter, you can look at the corresponding Python scripts to understand exactly how the ideas translate into real instructions a computer can follow. You might use this to train a simple network on handwritten digits, for example, or to see how different design choices affect how well the network learns. This would be valuable if you're a student, someone transitioning into machine learning, or a developer who wants to understand neural networks from first principles rather than just using a pre-built tool. The author notes that the code is stable and meant to match the book, so you won't see constant updates, but if you find bugs, you're welcome to report them or fork the code to modify it for your own purposes. The MIT license means you can use and adapt the code freely.