Deep learning
By: Kelleher, John D.
Series: The MIT press essential knowledge series. Publisher: Cambridge MIT Press 2019Description: x, 280p.ISBN: 9780262537551.Subject(s): Machine learning | Artificial intelligenceDDC classification: 006.31 | K28d Summary: 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.| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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PK Kelkar Library, IIT Kanpur | General Stacks | 006.31 K28d cop.1 (Browse shelf) | Copy.1 | Checked out to Prashant Kumar (S2220126400) | 18/05/2024 | A185745 | |
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Books
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PK Kelkar Library, IIT Kanpur | General Stacks | 006.31 K28d cop.2 (Browse shelf) | Copy.2 | Checked out to MOHAN KRISHNA (S20059000) | 15/05/2024 | A186029 |
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
| 006.31 H279e2 The elements of statistical learning | 006.31 H859m Machine learning methods in the environmental sciences | 006.31 K28d cop.1 Deep learning | 006.31 K28d cop.2 Deep learning | 006.31 K459 Kernel-based data fusion for machine learning | 006.31 K77m Machine learning | 006.31 K959e An elementary introduction to statistical learning theory |
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.
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