Learning to Learn
Sebastian Thrun ยท Lorien Pratt
เชกเชฟเชธเซ 2012 ยท Springer Science & Business Media
เช-เชชเซเชธเซเชคเช
354
เชชเซเช
reportเชฐเซเชเชฟเชเช เช
เชจเซ เชฐเชฟเชตเซเชฏเซ เชเชเชพเชธเซเชฒเชพ เชจเชฅเซย เชตเชงเซ เชเชพเชฃเซ
เช เช-เชชเซเชธเซเชคเช เชตเชฟเชถเซ
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications.
Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it.
To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing.
A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications.
Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.
Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it.
To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing.
A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications.
Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.
เช เช-เชชเซเชธเซเชคเชเชจเซ เชฐเซเชเชฟเชเช เชเชชเซ
เชคเชฎเซ เชถเซเช เชตเชฟเชเชพเชฐเซ เชเซ เช
เชฎเชจเซ เชเชฃเชพเชตเซ.
เชฎเชพเชนเชฟเชคเซ เชตเชพเชเชเชตเซ
เชธเซเชฎเชพเชฐเซเชเชซเซเชจ เช
เชจเซ เชเซ
เชฌเซเชฒเซเช
Android เช
เชจเซ iPad/iPhone เชฎเชพเชเซ Google Play Books เชเชช เชเชจเซเชธเซเชเซเชฒ เชเชฐเซ. เชคเซ เชคเชฎเชพเชฐเชพ เชเชเชพเชเชจเซเช เชธเชพเชฅเซ เชเชเซเชฎเซ
เชเชฟเช เชฐเซเชคเซ เชธเชฟเชเช เชฅเชพเชฏ เชเซ เช
เชจเซ เชคเชฎเชจเซ เชเซเชฏเชพเช เชชเชฃ เชนเซ เชคเซเชฏเชพเช เชคเชฎเชจเซ เชเชจเชฒเชพเชเชจ เช
เชฅเชตเชพ เชเชซเชฒเชพเชเชจ เชตเชพเชเชเชตเชพเชจเซ เชฎเชเชเซเชฐเซ เชเชชเซ เชเซ.
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เชจเซ เชเชฎเซเชชเซเชฏเซเชเชฐ
Google Play เชชเชฐ เชเชฐเซเชฆเซเชฒ เชเชกเชฟเชเชฌเซเชเชจเซ เชคเชฎเซ เชคเชฎเชพเชฐเชพ เชเชฎเซเชชเซเชฏเซเชเชฐเชจเชพ เชตเซเชฌ เชฌเซเชฐเชพเชเชเชฐเชจเซ เชเชชเชฏเซเช เชเชฐเซเชจเซ เชธเชพเชเชญเชณเซ เชถเชเซ เชเซ.
eReaders เช
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เชจเซเชฏ เชกเชฟเชตเชพเชเชธ
Kobo เช-เชฐเซเชกเชฐ เชเซเชตเชพ เช-เชเชเช เชกเชฟเชตเชพเชเชธ เชชเชฐ เชตเชพเชเชเชตเชพ เชฎเชพเชเซ, เชคเชฎเชพเชฐเซ เชซเชพเชเชฒเชจเซ เชกเชพเชเชจเชฒเซเชก เชเชฐเซเชจเซ เชคเชฎเชพเชฐเชพ เชกเชฟเชตเชพเชเชธ เชชเชฐ เชเซเชฐเชพเชจเซเชธเชซเชฐ เชเชฐเชตเชพเชจเซ เชเชฐเซเชฐ เชชเชกเชถเซ. เชธเชชเซเชฐเซเชเซเชก เช-เชฐเซเชกเชฐ เชชเชฐ เชซเชพเชเชฒเซ เชเซเชฐเชพเชจเซเชธเซเชซเชฐ เชเชฐเชตเชพ เชฎเชพเชเซ เชธเชนเชพเชฏเชคเชพ เชเซเชจเซเชฆเซเชฐเชจเซ เชตเชฟเชเชคเชตเชพเชฐ เชธเซเชเชจเชพเช เช
เชจเซเชธเชฐเซ.
