Math and Architectures of Deep Learning

The book "Math and Architectures of Deep Learning" provides a bridge between theory and practice, explaining the math of deep learning alongside practical implementations in Python and PyTorch. It helps engineers understand how their models work and enables them to build, customize, and re-architect DL models for specific needs. The book covers basic algebra, calculus, and statistics and introduces state-of-the-art DL paradigms from recent research.

Format: Paperback / softback
Length: 450 pages
Publication date: 15 March 2024
Publisher: Manning Publications

The mathematical paradigms that underpin deep learning often begin as dense, academic papers, leaving engineers in the dark about the inner workings of their models. To bridge this gap, Math and Architectures of Deep Learning provides a comprehensive guide that combines theoretical explanations with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, this book delves into the "black box" of deep learning, enabling readers to understand how their code functions and comprehend cutting-edge research that can be translated into practical applications.

Understanding the technology behind deep learning is crucial for maintaining models efficiently and communicating their results to stakeholders. While the mathematical foundations and neural network architecture can be complex, the rewards are significant. By gaining a deep understanding of these concepts, engineers can break free from the limitations of pre-packaged DL models and develop, customize, and re-architect solutions tailored to their specific needs. This independence also empowers them to identify and resolve problems quickly, ensuring the reliability and effectiveness of their deep learning systems.

Math and Architectures of Deep Learning is designed to be both useful and accessible to working practitioners. Each chapter explores a new fundamental deep learning concept or architectural pattern, providing clear explanations of the underlying mathematics and demonstrating their practical application through well-annotated Python code. The book begins with a primer on basic algebra, calculus, and statistics, building a strong foundation for subsequent chapters that delve into state-of-the-art deep learning paradigms. By the end of the book, readers will have a comprehensive theoretical understanding and practical skills to identify and implement deep learning architecture for a wide range of real-world challenges.

In conclusion, Math and Architectures of Deep Learning is an essential resource for anyone seeking to excel in the field of deep learning. By bridging the gap between theory and practice, this book empowers engineers to understand the inner workings of their models, comprehend cutting-edge research, and develop practical solutions that can drive innovation and progress in their industries. Whether you are a seasoned practitioner or just starting your journey into deep learning, this book will be your guide to unlocking the full potential of this powerful technology.

Weight: 998g
Dimension: 235 x 190 x 30 (mm)
ISBN-13: 9781617296482