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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
–Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Explore the cutting-edge field of artificial intelligence with the hardcover edition of Deep Learning from the Adaptive Computation and Machine Learning series. Co-authored by industry-renowned experts, this book serves as the definitive guide for understanding and mastering deep learning concepts, algorithms, and techniques. Whether you’re a data scientist, AI practitioner, or an academic researcher, this book is designed to meet your needs.
This meticulously crafted book delves into key topics in deep learning, including neural networks, optimization methods, sequence modeling, and probabilistic models. With step-by-step explanations grounded in real-world applications, readers will gain a deep appreciation of the technical intricacies of AI systems. Perfectly suited for beginners and seasoned experts alike, this hardcover edition synthesizes complex concepts into accessible knowledge. It also bridges the gap between foundational principles and impactful industry applications.
The hardcover format offers durability and a professional aesthetic, making it ideal for long-term reference and inclusion in your library. By choosing the Adaptive Computation and Machine Learning series, you’re investing in a trusted resource praised by academics and practitioners globally. High-quality printing and binding ensure that this book will stand the test of time, while the content provides invaluable insights that remain relevant in the rapidly evolving AI landscape.
Dive into the forefront of machine learning and deepen your understanding with the Adaptive Computation and Machine Learning series: Deep Learning (Hardcover). Order today and experience the transformative power of AI education through this exceptional volume.
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