Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

AI research has made significant breakthroughs, but deciphering them often requires a Ph.D. education in machine learning and mathematics. This updated second edition describes the intuition behind these innovations without the jargon and complexity, and by the end of the book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement them on their own and reason about them with a level of sophistication that rivals some of the best in the field.

Format: Paperback / softback
Length: 450 pages
Publication date: 27 May 2022
Publisher: O'Reilly Media, Inc, USA

We are currently experiencing a remarkable surge in AI research, driven by the groundbreaking advancements of deep learning. This revolutionary technology has enabled us to achieve remarkable feats, such as developing self-driving vehicles that surpass human capabilities, defeating human experts in complex games like Go and Starcraft, and even generating essays with remarkably coherent prose. However, the intricacies of these breakthroughs often require a deep understanding of machine learning and mathematics, making them accessible only to those with advanced degrees in these fields.

In response to this challenge, this updated second edition of the book aims to demystify these innovations and provide a clear and accessible explanation for programmers, software engineering professionals, and computer science majors. By the end of this comprehensive guide, readers will possess the skills necessary to re-implement these breakthroughs on their own and engage in sophisticated reasoning about them, rivaling some of the best experts in the field.

The book begins by introducing the fundamental concepts of deep learning and explaining how they have revolutionized the field. It then delves into the latest advancements in generative modeling and interpretability, two areas that are gaining increasing importance in AI research. Throughout the book, code examples are updated to TensorFlow 2 and PyTorch 1.4, ensuring that readers are working with the most recent tools and technologies.

In addition to its technical content, the book also includes valuable insights and anecdotes from leading experts in the field. These insights provide a deeper understanding of the challenges and opportunities that arise in AI research, and offer valuable guidance for those who are aspiring to contribute to this rapidly evolving field.

Whether you are a seasoned programmer looking to expand your skills or a student embarking on a career in computer science, this book is an essential resource for anyone who wants to stay at the forefront of AI research. With its clear and concise explanations, comprehensive code examples, and valuable insights from industry experts, it provides the foundation necessary to unlock the full potential of deep learning and drive innovation in the field. So, why wait? Start your journey into the world of AI today and unlock the secrets of this revolutionary technology with this updated second edition of "Deep Learning: A Beginner's Guide."

Dimension: 232 x 178 (mm)
ISBN-13: 9781492082187
Edition number: 2 New edition