Practical Deep Reinforcement Learning With Python
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Practical Deep Reinforcement Learning With Python
$13.41
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$13.41
Regular price
$13.41
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Description
Introducing Practical Smart Agents Development Using Python, Pytorch, And Tensorflow Description
Reinforcement Learning Is A Fascinating Branch Of Ai That Differs From Standard Machine Learning In Several Ways. Adaptation And Learning In An Unpredictable Environment Is The Part Of This Project. There Are Numerous Real-World Applications For Reinforcement Learning These Days, Including Medical, Gambling, Human Imitation Activity, And Robotics.
This Book Introduces Readers To Reinforcement Learning From A Pragmatic Point Of View. The Book Does Involve Mathematics, But It Does Not Attempt To Overburden The Reader, Who Is A Beginner In The Field Of Reinforcement Learning.
The Book Brings A Lot Of Innovative Methods To The ReaderS Attention In Much Practical Learning, Including Monte-Carlo, Deep Q-Learning, Policy Gradient, And Actor-Critical Methods. While You Understand These Techniques In Detail, The Book Also Provides A Real Implementation Of These Methods And Techniques Using The Power Of Tensorflow And Pytorch. What You Will Learn
Familiarize Yourself With The Fundamentals Of Reinforcement Learning And Deep Reinforcement Learning.
Make Use Of Python And Gym Framework To Model An External Environment.
Apply Classical Q-Learning, Monte Carlo, Policy Gradient, And Thompson Sampling Techniques. Explore Tensorflow And Pytorch To Practice The Fundamentals Of Deep Reinforcement Learning.
Design A Smart Agent For A Particular Problem Using A Specific Technique. Who This Book Is For
This Book Is For Machine Learning Engineers, Deep Learning Fanatics, Ai Software Developers, Data Scientists, And Other Data Professionals Eager To Learn And Apply Reinforcement Learning To Ongoing Projects. No Specialized Knowledge Of Machine Learning Is Necessary; However, Proficiency In Python Is Desired.
Reinforcement Learning Is A Fascinating Branch Of Ai That Differs From Standard Machine Learning In Several Ways. Adaptation And Learning In An Unpredictable Environment Is The Part Of This Project. There Are Numerous Real-World Applications For Reinforcement Learning These Days, Including Medical, Gambling, Human Imitation Activity, And Robotics.
This Book Introduces Readers To Reinforcement Learning From A Pragmatic Point Of View. The Book Does Involve Mathematics, But It Does Not Attempt To Overburden The Reader, Who Is A Beginner In The Field Of Reinforcement Learning.
The Book Brings A Lot Of Innovative Methods To The ReaderS Attention In Much Practical Learning, Including Monte-Carlo, Deep Q-Learning, Policy Gradient, And Actor-Critical Methods. While You Understand These Techniques In Detail, The Book Also Provides A Real Implementation Of These Methods And Techniques Using The Power Of Tensorflow And Pytorch. What You Will Learn
Familiarize Yourself With The Fundamentals Of Reinforcement Learning And Deep Reinforcement Learning.
Make Use Of Python And Gym Framework To Model An External Environment.
Apply Classical Q-Learning, Monte Carlo, Policy Gradient, And Thompson Sampling Techniques. Explore Tensorflow And Pytorch To Practice The Fundamentals Of Deep Reinforcement Learning.
Design A Smart Agent For A Particular Problem Using A Specific Technique. Who This Book Is For
This Book Is For Machine Learning Engineers, Deep Learning Fanatics, Ai Software Developers, Data Scientists, And Other Data Professionals Eager To Learn And Apply Reinforcement Learning To Ongoing Projects. No Specialized Knowledge Of Machine Learning Is Necessary; However, Proficiency In Python Is Desired.
Product Notice
This book is sold in used condition unless explicitly stated as new.
Condition is graded and described accurately. Some books may contain
previous owner's markings, highlights, or inscriptions. This product
may contain chemicals known to the State of California to cause cancer
or reproductive harm. For more information visit
www.P65Warnings.ca.gov
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