Python machine learning

"There are words that are so familiar they obscure rather than illuminate the thing they mean, and 'learning' is such a word. It seems so ordinary, everyone does it. Actually it's more of a black box,

The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.This textbook offers a comprehensive and self-co

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

強化學習是一類重要的機器學習方法,在很多領域得到了成功的應用,*近幾年與深度學習結合起來,進一步推動了人工智能的發展?本書首先介紹了強化學習的基本原理,然後介紹典型的強化學習算法,包括時序差分? SARSA? Q-Learning? DeepQ-network? Double DQN?競爭網絡結構? Rainbow? Actor-Critic? A2C? A3C? TRPO和PPO等,每種算法基本

An illuminating dive into the latest science on our brain’s remarkable learning abilities and the potential of the machines we program to imitate themThe human brain is an extraordinary machine. Its a

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.