Deep Reinforcement Learning Hands-On pdf epub mobi txt 电子书 下载 2024


Deep Reinforcement Learning Hands-On

简体网页||繁体网页
Maxim Lapan
Packt Publishing
2018-6-21
546
USD 35
Paperback
9781788834247

图书标签: 强化学习  机器学习  AI  美国  历史  ML  2020   


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发表于2024-12-23

Deep Reinforcement Learning Hands-On epub 下载 mobi 下载 pdf 下载 txt 电子书 下载 2024

Deep Reinforcement Learning Hands-On epub 下载 mobi 下载 pdf 下载 txt 电子书 下载 2024

Deep Reinforcement Learning Hands-On pdf epub mobi txt 电子书 下载 2024



图书描述

Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

Deep Reinforcement Learning Hands-On 下载 mobi epub pdf txt 电子书

著者简介

Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lays from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, Machine Learning, and large parallel distributed HPC and nonHPC systems, he has a talent to explain a gist of complicated things in simple words and vivid examples. His current areas of interest lie in practical applications of Deep Learning, such as Deep Natural Language Processing and Deep Reinforcement Learning. Maxim lives in Moscow, Russian Federation, with his family, and he works for an Israeli start-up as a Senior NLP developer.


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