Machine Learning: The New AI
The MIT Press Essential Knowledge Series
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Audible会員プラン 無料体験
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ナレーター:
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Steven Menasche
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著者:
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Ethem Alpaydi
このコンテンツについて
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition - as well as some we don't yet use every day, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning - the foundation of efforts to process that data into knowledge - has also advanced.
In this audiobook, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general listener, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
©2016 Massachusetts Institute of Technology (P)2016 Gildan Media LLCこちらもおすすめ
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第三次人工知能(AI)ブームの中核的役割を果たす深層学習(ディープ・ラーニング)は、その高い信頼性と汎用性ゆえに様々な領域に応用されていく一方で、
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Peter Seibel interviews 15 of the most interesting computer programmers alive today in Coders at Work, offering a companion volume to Apress’ highly acclaimed best-seller Founders at Work by Jessica Livingston. As the words “at work” suggest, Peter Seibel focuses on how his interviewees tackle the day-to-day work of programming, while revealing much more, like how they became great programmers, how they recognize programming talent in others, and what kinds of problems they find most interesting.
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