Deep Learning
sht 2019 · MIT Press
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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.
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.
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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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