Dive into Deep Learning

Deep learning has fundamentally transformed pattern recognition, driving advancements in diverse fields such as computer vision, natural language processing, and automatic speech recognition. This book serves as a comprehensive resource designed to make these complex technologies approachable for a wide audience, including engineers, scientists, and students. It addresses the multifaceted requirements of applying deep learning, which involves understanding problem formulation, mathematical modeling, model fitting algorithms, and the engineering techniques necessary for implementation. The text is structured to be accessible to readers with no prior background in machine learning or deep learning, explaining every concept from the ground up. To support this learning process, the book includes an appendix that acts as a refresher on the essential mathematics required to understand the material. By balancing technical depth with clarity, the guide ensures that readers gain the necessary skills to apply deep learning methods effectively in their own professional or academic work. A key feature of this resource is the inclusion of runnable code throughout the chapters, allowing readers to build intuition by putting theoretical ideas into practice. This hands-on approach helps bridge the gap between abstract concepts and real-world application. Whether for research or practical engineering, this book provides the foundational knowledge and technical tools needed to master the algorithms and modeling techniques that power modern deep learning technologies.