Deep Learning Adaptive Computation and Machine Learning series Ian Goodfellow Yoshua Bengio Aaron Courville 9780262035613 Books Télécharger i ZMG

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Téléchargez le PDF Deep Learning Adaptive Computation and Machine Learning series Ian Goodfellow Yoshua Bengio Aaron Courville 9780262035613 Books YDX

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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.

Ian Goodfellow, Yoshua Bengio, Aaron Courville,Deep Learning (Adaptive Computation and Machine Learning series),The MIT Press,0262035618,General,Machine learning.,Artificial Intelligence,COMPUTERS,COMPUTERS / Artificial Intelligence / General,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,Computer Applications,Computer Science,Computer Science/General,Computer Science/Machine Learning Neural Networks,Computers/Computer Science,EDUCATION / General,Education,Machine Learning Neural Networks,Machine learning,Non-Fiction,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States,probability for statistics and machine learning; murphy machine learning; python machine learning; deep neural networks; neural networks comprehensive; neural networks java; stacking machine learning; machine learning robotics; turing machine learning; bishop machine; lasso machine learning; machine learning python; feature engineering machine learning; scaling up machine learning; computer science; neural networks c; verts computer science; theoretical computer science; machine learning an algorithmic perspective; anomaly detection machine learning; azure machine learning; mit computer,probability for statistics and machine learning;murphy machine learning;python machine learning;deep neural networks;neural networks comprehensive;neural networks java;stacking machine learning;machine learning robotics;turing machine learning;bishop machine;lasso machine learning;machine learning python;feature engineering machine learning;scaling up machine learning;computer science;neural networks c;verts computer science;theoretical computer science;machine learning an algorithmic perspective;anomaly detection machine learning;azure machine learning;mit computer,COMPUTERS / Artificial Intelligence / General,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,Computers/Computer Science,EDUCATION / General,Education,Computer science,Machine learning

Deep Learning Adaptive Computation and Machine Learning series Ian Goodfellow Yoshua Bengio Aaron Courville 9780262035613 Books Reviews :

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.

Ian Goodfellow, Yoshua Bengio, Aaron Courville,Deep Learning (Adaptive Computation and Machine Learning series),The MIT Press,0262035618,General,Machine learning.,Artificial Intelligence,COMPUTERS,COMPUTERS / Artificial Intelligence / General,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,Computer Applications,Computer Science,Computer Science/General,Computer Science/Machine Learning Neural Networks,Computers/Computer Science,EDUCATION / General,Education,Machine Learning Neural Networks,Machine learning,Non-Fiction,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States,probability for statistics and machine learning; murphy machine learning; python machine learning; deep neural networks; neural networks comprehensive; neural networks java; stacking machine learning; machine learning robotics; turing machine learning; bishop machine; lasso machine learning; machine learning python; feature engineering machine learning; scaling up machine learning; computer science; neural networks c; verts computer science; theoretical computer science; machine learning an algorithmic perspective; anomaly detection machine learning; azure machine learning; mit computer,probability for statistics and machine learning;murphy machine learning;python machine learning;deep neural networks;neural networks comprehensive;neural networks java;stacking machine learning;machine learning robotics;turing machine learning;bishop machine;lasso machine learning;machine learning python;feature engineering machine learning;scaling up machine learning;computer science;neural networks c;verts computer science;theoretical computer science;machine learning an algorithmic perspective;anomaly detection machine learning;azure machine learning;mit computer,COMPUTERS / Artificial Intelligence / General,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,Computers/Computer Science,EDUCATION / General,Education,Computer science,Machine learning

Deep Learning (Adaptive Computation and Machine Learning series) [Ian Goodfellow, Yoshua Bengio, Aaron Courville] on . PBAn introduction to a broad range of topics in deep learning, covering mathematical and conceptual background

 

Product details Series Adaptive Computation and Machine Learning series Hardcover 800 pages Publisher The MIT Press (November 18, 2016) Language English ISBN-10 0262035618

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