MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02731 a2200193 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
240424b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789355512055 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.74015 |
| Cutter |
GRI |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gridin Ivan |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
BPB Publications |
| Date of publication, distribution, etc |
2022 |
| Place of publication, distribution, etc |
London |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
377 |
| 520 ## - Remark |
|---|
| Summary, etc |
Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow<br/>Description<br/>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.<br/><br/>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.<br/><br/>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.<br/>What you will learn<br/>● Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning.<br/>● Make use of Python and Gym framework to model an external environment.<br/>● Apply classical Q-learning, Monte Carlo, Policy Gradient, and Thompson sampling techniques. ● Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning.<br/>● Design a smart agent for a particular problem using a specific technique.<br/>Who this book is for<br/>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.<br/><br/>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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Algorithms |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Simplified Maths |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
Business Analytics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
