Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch

By:

Gridin Ivan

Publication details: BPB Publications 2022 LondonDescription: 377ISBN:

9789355512055

Subject(s):

DDC classification:

005.74015 GRI

Summary: 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 reader's 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. Source: https://www.amazon.in/Practical-Deep-Reinforcement-Learning-Python/dp/9355512058/ref=sr_1_1?crid=249NYB5UC57BF&dib=eyJ2IjoiMSJ9.4Xq7KQuhv2vlEAV72I_sTw.Kx7svcw5ifrzRhW0R7Dma4FVMT-myRp6Ir79DrGkoOs&dib_tag=se&keywords=9789355512055&qid=1713968042&sprefix=9788196010065%2Caps%2C476&sr=8-1

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Main Library Analytics	Reference book	005.74015 GRI (Browse shelf(Opens below))	Available		119396

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 reader's 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.

Source: https://www.amazon.in/Practical-Deep-Reinforcement-Learning-Python/dp/9355512058/ref=sr_1_1?crid=249NYB5UC57BF&dib=eyJ2IjoiMSJ9.4Xq7KQuhv2vlEAV72I_sTw.Kx7svcw5ifrzRhW0R7Dma4FVMT-myRp6Ir79DrGkoOs&dib_tag=se&keywords=9789355512055&qid=1713968042&sprefix=9788196010065%2Caps%2C476&sr=8-1

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