The StatQuest Illustrated Guide to Neural Networks and AI: With hands-on examples in PyTorch!!!

AI applications like ChatGPT can seem almost magical. A simple prompt can generate a new poem, or even write code for you. But how does this magic happen? That’s where The StatQuest Illustrated Guide to Neural Networks and AI comes in. This book takes the algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each concept is clearly illustrated to provide you with an intuition about how the methods work that goes beyond the equations alone. The StatQuest Illustrated Guide does not dumb down the concepts. Instead, it builds you up, so you’re smarter and have a deeper understanding of neural networks and AI.

The StatQuest Illustrated Guide to Neural Networks and AI starts with the basics, showing you how a simple neural network fits a shape to data and then builds from there, one step at a time, until you have mastered the concepts behind today’s revolution in AI. Furthermore, putting the theory into practice is easy because each major concept is paired with a PyTorchtutorial that teaches you how to code each neural network from scratch.

Here’s what people are saying about The StatQuest Illustrated Guide to Neural Networks and AI:

This book is a game changer. With simple yet thorough diagrams, useful and relevant code examples, and his usual fun analogies, Starmer one more time manages to demystify the complexity behind neural networks. Topics such as transformers, LSTMs, and attention mechanisms will not be a mystery anymore after reading this gem. – Luis Serrano, Founder and CEO of Serrano Academy and author of Grokking Machine Learning

Fellow fans of StatQuest should rejoice for this accessible introduction to some of the most important concepts in AI. It reads like a playful comic that gently and visually guides you along the math that powers today’s AI revolution – Jay Alammar, author of The Illustrated Transformer and, with Maarten Grootendorst, Hands-On Large Language Models