Deep Learning for Coders with Fastai and PyTorch provides a hands-on, code-first approach to deep learning, making it accessible to programmers without requiring a PhD in mathematics. Written by Jeremy Howard, co-founder of Fastai, and Sylvain Gugger, a researcher at Hugging Face, this book introduces readers to practical AI applications using the Fastai library and PyTorch framework.

The book covers image classification, natural language processing (NLP), recommendation systems, and generative models, enabling readers to build and deploy deep learning models efficiently. By emphasizing real-world coding examples over complex theoretical derivations, the authors make AI more approachable for software developers.

Why Read This Book

• Learn deep learning through a code-first approach with Fastai and PyTorch.
• Build image recognition, NLP, and recommendation systems with minimal prerequisites.
• Develop and deploy AI models without needing a deep mathematical background.
• Understand best practices for model training, optimization, and interpretability.
• Written by leading AI experts, ensuring high-quality content and up-to-date practices.

About the Authors

Jeremy Howard is a co-founder of Fastai, an AI researcher, and an entrepreneur dedicated to making deep learning more accessible. He has worked in both academia and industry, contributing to AI education and practical AI advancements.

Sylvain Gugger is a researcher at Hugging Face, specializing in deep learning and NLP. He has been instrumental in the development of Fastai and has contributed to state-of-the-art AI models.