Setting Sun/st/spn/sampler Pk
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1997-11-01 |
Genre | : |
ISBN | : 9781572579491 |
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Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1997-11-01 |
Genre | : |
ISBN | : 9781572579491 |
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1997-10-01 |
Genre | : |
ISBN | : 9781572578739 |
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1997-10-01 |
Genre | : |
ISBN | : 9781572578654 |
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1997-10-01 |
Genre | : |
ISBN | : 9781572578678 |
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1997-10-01 |
Genre | : |
ISBN | : 9781572578692 |
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1998-08-01 |
Genre | : |
ISBN | : 9780769903798 |
Author | : Wright Group |
Publisher | : |
Total Pages | : |
Release | : 1998-08-01 |
Genre | : |
ISBN | : 9780769903897 |
Author | : Sarah Britton |
Publisher | : Clarkson Potter |
Total Pages | : 585 |
Release | : 2015-03-31 |
Genre | : Cooking |
ISBN | : 0804185395 |
At long last, Sarah Britton, called the “queen bee of the health blogs” by Bon Appétit, reveals 100 gorgeous, all-new plant-based recipes in her debut cookbook, inspired by her wildly popular blog. Every month, half a million readers—vegetarians, vegans, paleo followers, and gluten-free gourmets alike—flock to Sarah’s adaptable and accessible recipes that make powerfully healthy ingredients simply irresistible. My New Roots is the ultimate guide to revitalizing one’s health and palate, one delicious recipe at a time: no fad diets or gimmicks here. Whether readers are newcomers to natural foods or are already devotees, they will discover how easy it is to eat healthfully and happily when whole foods and plants are at the center of every plate.
Author | : Richard S. Sutton |
Publisher | : MIT Press |
Total Pages | : 549 |
Release | : 2018-11-13 |
Genre | : Computers |
ISBN | : 0262352702 |
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author | : Marc Peter Deisenroth |
Publisher | : Cambridge University Press |
Total Pages | : 392 |
Release | : 2020-04-23 |
Genre | : Computers |
ISBN | : 1108569323 |
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.