Quan Sen (154 results)

paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Condition: New. pp. viii + 82 Index.

Condition: New. pp. viii + 82 Illus.

Paperback. Condition: Brand New. 82 pages. 8.82x5.91x0.32 inches. In Stock.

Condition: New. pp. viii + 82.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems (Paperback or Softback).

paperback. Condition: New. Language:Chinese.Calligraphers five Jie ( right Xijun Zhangwan Qing Li Duo Zhongming Shan Liu Bingsen ) [Genuine Big 16 nearly 10 products ].

Condition: New. In.

Paperback. Condition: New.

Paperback. Condition: New. Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is forIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

paperback. Condition: New. Language:Chinese.The specific content of the paperback. Pages. Number: 300 consulting the advice of the master consists of five parts: (1) the purpose and process. involving the practical steps to establish an effective consultation relationship. and defines the specific steps of the consultation. (2) and interpersonal skills. with emphasis. in consultation with the relationship between the individual and interpersonal relationships. and through examples of how to deal with specific issues of.

Condition: New.

Paperback. Condition: New. Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is forIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

Condition: As New. Unread book in perfect condition.

Soft cover. Condition: New. Language:Chinese.Author:PANG SEN QUAN DENG.Binding:Soft cover.Publisher:China Social Publishing House Pub. Date :2010-10-0.

Soft cover. Condition: New. Language:Chinese.Author:( FA ) A LAN. DE BU WA XI E ZHU YU DE QUAN / JIN SEN DENG YI.Binding:.Publisher:World Knowledge Publishing House.

Soft cover. Condition: New. Language:Chinese.Author:ZHANG QUAN ZHI ZHANG YONG PENG SONG FU SEN BIAN ZHU.Binding:.Publisher:Shandong People.

paperback. Condition: New. Paperback. Pub Date: 2022-01-01 Pages: 210 Language: Chinese Publisher: Huazhong Normal University Press The book is divided into 5 parts: the kinetic theory of molecules. gases. solids and liquids. the laws of thermodynamics. atomic structure and wave-particle duality. and atomic nuclei. Among them. the kinetic theory of molecules and the equation of state of ideal gas are the basic contents of thermology. It is necessary to master the characteristics of intermolecular forces and the underst.

paperback. Condition: New. Paperback. Pub Date :2013-11-01 Pages: 331 Language: Chinese Publisher: Educational Science Publishing House. China Compulsory Education Development Report (2012) is divided into five parts: The first part reviews the 1986 duty People's Republic of China Since the Education Law enacted in 2011. nine years China fully completed 25 years of development history of China's obligations context. the main achievements and problems. The second part of the overall analysis of the foundation in 2011 Ch.

Hardcover. Condition: Good. HardCover. Pub Date :1988-06 Pages: 141 Publisher: Liaoning Science and Technology Press sheets: 112.

paperback. Condition: New. Paperback.Pub Date:2016-07 Pages:128 Language:Chinese Publisher:Suzhou University Press Gulliver's Travels is an outstanding travelogue-style satirical novel by Jonathan Swift. The work uses satire and allusion to reflect the social contradictions in Britain in the first half of the 18th century through Gulliver's adventures in Lilliput. Brobdingnag. Laputa. and Houyhnhnms. exposing the corruption and crimes of the ruling group at that time. and criticizing aggressive wars and colonialism. Th.

Paperback. Condition: New. Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is forIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

Hardcover. Condition: New. Language:Chinese.HardCover.Pub Date:2024-02 Pages:224 Publisher:CITIC Press:Why are China and the United States destined to coexist? How do you view the state of bilateral competition between China and the United States? What factors affect the probability of war? Where is the way to cross the trap hidden? What is the practical significance of the Thucydides Trap to the world in the 21st century? As a framework widely cited by decision-makers at home and abroad and the general public when und.

Soft cover. Condition: New. Language:Chinese.Author:YANG FU JIA WANG YAN SEN LU FU QUAN LIN YAO HUA.Binding:Soft cover.Publisher:Fudan University Press Pub. Date :2006-03-01.

paperback. Condition: Good. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Young people's ideological and political education of the western regionFour Satisfaction guaranteed,or money back.

Soft cover. Condition: New. Language:Chinese.Author:(MEI)DENG PU DUN AI LI SEN YING SHAO QUAN SU ZHI MIN YI.Binding:Soft cover.Publisher:Shanghai Jiaotong University Press Pub. Date :2010.

paperback. Condition: New. Paperback. Pub Date: 2012 Pages: 92 Language: Chinese Publisher: Yanbian Education Press century GOLDBOND: Dr. Jekyll and Mr. Hyde. Robert Louis Stevenson's famous for. the representative. century GOLDBOND: Dr. Jekyll and Mr. Hyde new romanticism as a model of literature. The bizarre plot. the fascinating story of one of the world's best-loved literary classics. Dr. Jekyll and Mr. Hyde bizarre and extraordinary imagination performance of deep suspicion of human nature. dripping depicts human .

Paperback. Condition: New. Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is forIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.