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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.
Deep Learning, part of the renowned Adaptive Computation and Machine Learning series, is a must-have paperback for anyone eager to explore the rapidly evolving field of artificial intelligence and its foundational concepts. Designed for both beginners and seasoned experts, this book bridges the gap between complex academic theories and practical applications, making it an invaluable resource for those passionate about data science, machine learning, and computational intelligence.
This paperback walks readers through the essential principles of deep learning, serving as both an introductory reference and a detailed compendium for more advanced learners. Key topics including neural networks, optimization techniques, unsupervised learning methods, and generative models are explained with clarity. Authored by industry leaders, the content is meticulously crafted to ensure an impactful learning experience, backed by practical examples and intuitive explanations.
Whether you’re delving into deep learning for academic purposes, professional advancement, or personal interest, the organizational structure of this paperback makes it an ideal reference. Chapters flow seamlessly, providing readers with a cohesive roadmap to understanding foundational and advanced concepts. The blend of theoretical rigor with real-world applications allows learners to confidently implement deep learning techniques for problem-solving in diverse domains such as natural language processing, computer vision, and robotics.
The paperback edition of Deep Learning offers portability and convenience, enabling you to access this invaluable resource wherever you go. The durable format ensures longevity, making it suitable for extensive study and reference. This comprehensive learning guide is also part of the Adaptive Computation and Machine Learning series, known for its authoritative texts on modern computational strategies. Optimize your learning experience while enjoying the practicality of a paperback format.
Strengthen your grasp of deep learning methodologies with this essential paperback. Whether you’re a student, researcher, engineer, or AI enthusiast, Deep Learning from the Adaptive Computation and Machine Learning series ensures you stay ahead in the fast-paced field of artificial intelligence.
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