An Introduction to. Computational Learning Theory. Michael J. Kearns. Umesh V. Vazirani. The MIT Press. Cambridge, Massachusetts. London, England.
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Deep learning is a class of machine learning algorithms that( pp199–200) uses multiple layers to progressively extract higher level features from the raw input. Learning attempts to reduce the total of the differences across the observations. Most learning models can be viewed as a straightforward application of optimization theory and statistical estimation. Integers can be considered either in themselves or as solutions to equations (Diophantine geometry). Questions in number theory are often best understood through the study of analytical objects (for example, the Riemann zeta function) that… Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. syllabus PCD - Free download as PDF File (.pdf), Text File (.txt) or read online for free.
Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a few significant themes in computational studying conception for researchers and scholars in man made intelligence, neural networks, theoretical…
