Additional information
Full Title | Deep Learning |
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Author(s) | John D. Kelleher |
Edition | |
ISBN | 9780262354905, 9780262537551 |
Publisher | The MIT Press |
Format | PDF and EPUB |
Original price was: $13.99.$3.50Current price is: $3.50.
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Full Title | Deep Learning |
---|---|
Author(s) | John D. Kelleher |
Edition | |
ISBN | 9780262354905, 9780262537551 |
Publisher | The MIT Press |
Format | PDF and EPUB |
DEEP LEARNING FOR BEGINNERS: Get an accessible introduction to the artificial intelligence technology at the heart of the AI revolution—from driverless cars to speech recognition on mobile phones. Deep learning is an artificial intelligence technology that enables computer vision, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets. Its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures—including autoencoders, recurrent neural networks, and long short-term networks—as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—including major trends, possible developments, and significant challenges.
Original price was: $61.99.$24.99Current price is: $24.99.
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Full Title | Deep Learning |
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Author(s) | Ian Goodfellow, Yoshua Bengio, Aaron Courville |
Edition | |
ISBN | 9780262337373, 9780262035613 |
Publisher | The MIT Press |
Format | PDF and EPUB |
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.