An introduction to genetic algorithms / Melanie Mitchell. p. cm. "A Bradford book." Includes bibl... more An introduction to genetic algorithms / Melanie Mitchell. p. cm. "A Bradford book." Includes bibliographical references and index.
Machine learning blends concepts and techniques from many different traditional fields, such as m... more Machine learning blends concepts and techniques from many different traditional fields, such as mathematics, statistics, and computer science. As such, there are many ways to learn the discipline. Considering its theoretical foundations in mathematics and statistics, newcomers would do well to attain some degree of mastery of the formal specifications of basic machine learning techniques. There are many excellent books that focus on the fundamentals, the classic work being Hastie, Tibshirani, and Friedman's The Elements of Statistical Learning ([HTF09]; full references can be found in the Works Cited). 1 But another important part of the hacker mantra is to learn by doing. Many hackers may be more comfortable thinking of problems in terms of the process by which a solution is attained, rather than the theoretical foundation from which the solution is derived. From this perspective, an alternative approach to teaching machine learning would be to use "cookbook"-style examples. To understand how a recommendation system works, for example, we might provide sample training data and a version of the model, and show how the latter uses the former. There are many useful texts of this kind as well, and Segaran's Programming Collective Intelligence is one recent example [Seg07]. Such a discussion would certainly address the how of a hacker's method of learning, but perhaps less of the why. Along with understanding the mechanics of a method, we may also want to learn why it is used in a certain context or to address a specific problem. 1. The Elements of Statistical Learning can now be downloaded free of charge at http://www-stat.stanford .edu/~tibs/ElemStatLearn/.
Welcome to "Python Mastery: From Absolute Beginner to Pro"! If you're holding this book, chances ... more Welcome to "Python Mastery: From Absolute Beginner to Pro"! If you're holding this book, chances are you have an insatiable curiosity for Python programming, a language that has captivated the world of technology and innovation. Whether you're stepping into the exciting world of programming for the first time or seeking to refine your skills, you've come to the right place.
An introduction to genetic algorithms / Melanie Mitchell. p. cm. "A Bradford book." Includes bibl... more An introduction to genetic algorithms / Melanie Mitchell. p. cm. "A Bradford book." Includes bibliographical references and index.
Machine learning blends concepts and techniques from many different traditional fields, such as m... more Machine learning blends concepts and techniques from many different traditional fields, such as mathematics, statistics, and computer science. As such, there are many ways to learn the discipline. Considering its theoretical foundations in mathematics and statistics, newcomers would do well to attain some degree of mastery of the formal specifications of basic machine learning techniques. There are many excellent books that focus on the fundamentals, the classic work being Hastie, Tibshirani, and Friedman's The Elements of Statistical Learning ([HTF09]; full references can be found in the Works Cited). 1 But another important part of the hacker mantra is to learn by doing. Many hackers may be more comfortable thinking of problems in terms of the process by which a solution is attained, rather than the theoretical foundation from which the solution is derived. From this perspective, an alternative approach to teaching machine learning would be to use "cookbook"-style examples. To understand how a recommendation system works, for example, we might provide sample training data and a version of the model, and show how the latter uses the former. There are many useful texts of this kind as well, and Segaran's Programming Collective Intelligence is one recent example [Seg07]. Such a discussion would certainly address the how of a hacker's method of learning, but perhaps less of the why. Along with understanding the mechanics of a method, we may also want to learn why it is used in a certain context or to address a specific problem. 1. The Elements of Statistical Learning can now be downloaded free of charge at http://www-stat.stanford .edu/~tibs/ElemStatLearn/.
Welcome to "Python Mastery: From Absolute Beginner to Pro"! If you're holding this book, chances ... more Welcome to "Python Mastery: From Absolute Beginner to Pro"! If you're holding this book, chances are you have an insatiable curiosity for Python programming, a language that has captivated the world of technology and innovation. Whether you're stepping into the exciting world of programming for the first time or seeking to refine your skills, you've come to the right place.
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