Topic: Computing & Internet

Understanding Machine Learning

Understanding Machine Learning

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it...
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410 pages
2014-07-17 Published
Fairness and Machine Learning

Fairness and Machine Learning

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual...
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320 pages
2023-12-28 Published
An Introduction to Deep Reinforcement Learning

An Introduction to Deep Reinforcement Learning

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks...
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156 pages
2019-03-31 Published
Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.What distinguishes reinforcement learning from...
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104 pages
2010-06-25 Published
Distributional Reinforcement Learning

Distributional Reinforcement Learning

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for...
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400 pages
2023-05-30 Published
Reinforcement Learning, 2nd Edition

Reinforcement Learning, 2nd Edition

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...
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548 pages
2018-11-23 Published
Dive into Deep Learning

Dive into Deep Learning

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. Deep learning has revolutionized pattern recognition, introducing...
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574 pages
2024-02-01 Published
Foundations of Machine Learning, 2nd Edition

Foundations of Machine Learning, 2nd Edition

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve...
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504 pages
2019-04-02 Published
Approaching (Almost) Any Machine Learning Problem

Approaching (Almost) Any Machine Learning Problem

This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms...
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300 pages
2020-06-30 Published
Gradient Expectations

Gradient Expectations

An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others,...
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224 pages
2023-07-18 Published
Mathematical Analysis of Machine Learning Algorithms

Mathematical Analysis of Machine Learning Algorithms

The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI...
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479 pages
2023-08-10 Published
The Shallow and the Deep

The Shallow and the Deep

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the...
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290 pages
2023-09-27 Published