Deep Learning

· Gildan Media · 朗讀者:Joel Richards
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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 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 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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關於作者

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He is coauthor of Data Science and Fundamentals of Machine Learning for Predictive Data Analytics. Joel Richards was the kid who did crazy things just to have a good story to tell afterward. On deciding to make his affection his profession, he received a BFA in acting and a BA in English from the University of Utah. He has narrated over 150 audiobooks and continues to tell his original stories to live audiences.

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