Articles liés à Deep Learning
Synopsis
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.
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À propos de l?auteur
John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at Technological University Dublin. He is the coauthor of Data Science and the author of Deep Learning, both in the MIT Press Essential Knowledge series.
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Deep Learning (The MIT Press Essential Knowledge series)
Edité par
The MIT Press, 2019
ISBN 10 : 0262537559
ISBN 13 : 9780262537551
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Deep Learning (The MIT Press Essential Knowledge series)
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The MIT Press (edition Illustrated), 2019
ISBN 10 : 0262537559
ISBN 13 : 9780262537551
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Deep Learning
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Paperback. Etat : New. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, 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-major trends, possible developments, and significant challenges. N° de réf. du vendeur LU-9780262537551
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Deep Learning (The MIT Press Essential Knowledge series)
Edité par
The MIT Press, 2019
ISBN 10 : 0262537559
ISBN 13 : 9780262537551
Ancien ou d'occasion
Couverture souple
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Etat : very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if youâre not satisfied with purchase please return item for full refund. Ships USPS Media Mail. N° de réf. du vendeur OTV.0262537559.VG
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Deep Learning
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Paperback. Etat : New. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, 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-major trends, possible developments, and significant challenges. N° de réf. du vendeur LU-9780262537551
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Deep Learning
Edité par
MIT Press Ltd, 2019
ISBN 10 : 0262537559
ISBN 13 : 9780262537551
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Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days. 270. N° de réf. du vendeur B9780262537551
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Deep Learning
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Paperback. Etat : New. An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, 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-major trends, possible developments, and significant challenges. N° de réf. du vendeur LU-9780262537551
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Deep Learning
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur FV-9780262537551
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Deep Learning
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Etat : New. 2019. Illustrated. Paperback. . . . . . N° de réf. du vendeur V9780262537551
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DEEP LEARNING
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Etat : NEW. N° de réf. du vendeur NW9780262537551
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