ISBN 13: 9780135172490 . Main Foundations of Deep Reinforcement Learning: Theory and Practice in Python. Click on a series title to see the full list of products in the series. Graesser & Keng learn more. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Foundations of Deep Reinforcement Learning: Theory and Practice in Python. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Instant Access ISBNs are for individuals purchasing with credit cards or PayPal. Sort by. ISBN 10: 0135172497. Foundations of Deep Reinforcement Learning. Read "Foundations of Deep Reinforcement Learning Theory and Practice in Python" by Laura Graesser available from Rakuten Kobo. Series: Addison-Wesley data and analytics series. The following is a review of the book Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) by Laura Grasser and Wah Loon Keng, one of the best introductory books to the topic of Deep Reinforcement Learning.. Review. Foundations of Deep Reinforcement Learning This is the website for the book Foundations of Deep Reinforcement Learningby Laura Graesser and Wah Loon Keng. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. Wah Loon Keng ©2020 | Addison-Wesley Format: eBook-Multiformat ISBN-13: 9780135172506: Availability: Not yet available. In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. Learn about new offers and get more deals by joining our newsletter. We're sorry! It starts with intuition, then carefully explains the theory of deep RL algorithms, … Format: Paperback Change. Pearson offers special pricing when you package your text with other student resources. O’Reilly members experience live online training, … The print version of this textbook is ISBN: 9780135172384, 0135172381. Language: english. However, if you start looking into it then things get surprisingly mathematical very quickly. The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. Foundations of Deep Reinforcement Learning Theory and ~ Foundations of Deep Reinforcement Learning Theory and Practice in Python by Wah Loon Keng Laura Graesser Stay ahead with the worlds most comprehensive technology and business learning platform With Safari you learn the way you learn best Sign up now. Instant Access ISBNs are for individuals purchasing with credit cards or PayPal. ©2020 It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. ©2020, Download PowerPoint Lecture Slides (application/zip) Start your free trial. ©2020, Bridge Page t/a A First Course This is the website for the book Foundations of Deep Reinforcement Learning by Laura Graesser and Wah Loon Keng. Paperback. Spelling and grammar misprints are updated during the reprint process, but are not listed on this errata sheet. Get this from a library! Download books for free. Save US$10.97. Get Foundations of Deep Reinforcement Learning: Theory and Practice in Python now with O’Reilly online learning. Laura Graesser, Wah Loon Keng, "Foundations of Deep Reinforcement Learning: Theory and Practice in Python" ISBN: 0135172381 | 2019 | EPUB/PDF (conv) | 416 pages | 20 MB/13 MB. Year: 2020. Get this from a library! In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Foundations of Deep Reinforcement Learning: Theory and Practice in Python | Graesser, Laura;Keng, Wah Loon | download | Z-Library. Available in PDF, EPUB, and Mobi Format. Laura Graesser. We're sorry! Foundations of Deep Reinforcement Learning. Foundations of Deep Reinforcement Learning Theory and Practice in Python by Laura Graesser, Wah Loon Keng. US$39.08 US$50.05. This product is part of the following series. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. This eBook includes the following formats, accessible from your Account page after purchase: EPUB Grokking Deep Reinforcement Learning written by Miguel Morales and has been published by Manning Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories. |Addison-Wesley Professional Foundations of Deep Reinforcement Learning (Addison-Wesley) Wednesday, 11 December 2019 This introduction to deep reinforcement learning (RL) combines both theory and implementation. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning: Theory and Practice in Python Graesser, Laura, Keng, Wah Loon. Add to basket. Foundations of Deep Reinforcement Learning: Theory and Practice in Python, ©2020 |. Connect with us to It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Notify me. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Humanities & Social Sciences. Foundations of Deep Reinforcement Learning Theory and Practice in Python 1st Edition by Laura Graesser; Wah Loon Keng and Publisher Addison-Wesley Professional PTG. About us; Sitemap; Bookmarks; Sign in/Join; How can we … All reviewers. Introduction to Reinforcement Learning In this chapter we introduce the main concepts in reinforcement learning. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Find books In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. (17.0MB), Foundations of Deep Reinforcement Learning: Theory and Practice in Python Paperback. Sign up now. Foundations of Deep Reinforcement Learning: Theory and Practice in Python. Mail Foundations of Deep Reinforcement Learning Theory and Practice in Python (Addison-Wesley Data & Analytics Series) Graesser, Laura, Keng, Wah Loon Kindle Store Posted on in Uncategorized Publisher: Addison-Wesley Professional; 1 edition (November 20, 2019) Reinforcement learning is a simple idea - give the system a reward when it does well and let it adjust its behavior to maximize the reward. If you think the book is useful, feel free to recommend it to your friends, and add your review on Amazon! Foundations of Deep Reinforcement Learning: Theory and Practice in Python: Graesser, Laura, Keng, Wah Loon: 9780135172384: Books - Amazon.ca Foundations of deep reinforcement learning : theory and practice in Python. Companion Library: SLM Lab . It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This item is currently unavailable for purchase on our websites. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Everyday low prices and free delivery on eligible orders. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Learn about new offers and get more deals by joining our newsletter. Connect with us to This book … Within 3 days of receipt of your email, we will reply to let you know we got it. US$39.08 US$50.05. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence.Â, “This book provides an accessible introduction to deep reinforcement learning covering the mathematical concepts behind popular algorithms as well as their practical implementation. You have successfully signed out and will be required to sign back in should you need to download more resources. Add to basket. Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) eBook: Graesser, Laura, Keng, Wah Loon: Amazon.ca: Kindle Store Find many great new & used options and get the best deals for Foundations of Deep Reinforcement Learning by Laura Graesser Paperback at the best online prices at eBay! Notify me. 2. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer gamessuch as Go, … It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. I think the book will be a valuable resource for anyone looking to apply deep reinforcement learning in practice.”, Part I: Policy-Based and Value-Based Algorithms 23, PowerPoint Slides for Foundations of Deep Reinforcement Learning: Theory and Practice in Python. ©2008. Anthropology; Art; ... Graesser & Keng ©2020 Paper Series. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. Services . Click on a series title to see the full list of products in the series. Find books It is available on Amazon. 28 Jan 2020. unavailable. Laura Graesser. Foundations of Deep Reinforcement Learning - Theory and Practice in Python begins with a brief preliminary chapter, which serves to introduce a few concepts and terms that will be used throughout all the other chapters: agent, state, action, objective, reward, reinforcement, policy, value function, model, trajectory, transition. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. The Contemporary Introduction to Deep Reinforcement Learning … Foreword xixPreface xxiAcknowledgments xxvAbout the Authors xxviiChapter 1: Introduction to Reinforcement Learning 11.1 Reinforcement Learning 11.2 Reinforcement Learning as MDP 61.3 Learnable Functions in Reinforcement Learning 91.4 Deep Reinforcement Learning Algorithms 111.5 Deep Learning for Reinforcement Learning 171.6 Reinforcement Learning and Supervised Learning 191.7 Summary 21, PowerPoint Slides for Foundations of Deep Reinforcement Learning: Theory and Practice in Python Foundations of Deep Reinforcement Learning Theory and ~ Foundations of Deep Reinforcement Learning Theory and Practice in Python AddisonWesley Data Analytics Series Laura Graesser Wah Loon Keng on FREE shipping on qualifying offers The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning deep RL combines deep learning … Foundations of Deep Reinforcement Learning: Theory and Practice in Python, ©2020 The No Title® Tech Blog: Book review - Foundations of Deep Reinforcement Learning, by Laura Graesser and Wah Loon Keng Deep Reinforcement Learning is a somewhat new field within Machine Learning or Artificial Intelligence (you may pick your favorite term between these two, even if they’re not strictly the same), which combines Deep Learning and Reinforcement Learning and is … Get Foundations of Deep Reinforcement Learning: Theory and Practice in Python now with O’Reilly online learning. Foundations of Deep Reinforcement Learning Theory and Practice in Python (Addison-Wesley Data & Analytics Series) Graesser, Laura, Keng, Wah Loon 9780135172384 . Foundations Of Deep Reinforcement Learning. Savvas Learning Company is a trademark of Savvas Learning Company LLC. Des milliers de livres avec la livraison chez vous en 1 jour ou en magasin avec -5% de réduction . Navigate; Linked Data; Dashboard; Tools / Extras; Stats; Share . East Baton Rouge Parish Library. If you think the book is useful, feel free to recommend it to your friends, and add your review on Amazon! Get Foundations of Deep Reinforcement Learning: Theory and Practice in Python now with O’Reilly online learning. K12 Educators: Contact your Savvas Learning Company Account General Manager for purchase options. We start by looking at some simple examples to build intuitions about the core components of a reinforcement learning problem—namely, an agent and an environment. 01 Jan 2020. Foundations of deep reinforcement learning : theory and practice in Python. O’Reilly members experience live online training, … Foundations of Deep Reinforcement Learning. Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) by Graesser, Laura. PDF book with title Foundations Of Deep Reinforcement Learning by Laura Graesser suitable to read on your Kindle device, PC, phones or tablets. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Add to Cart. Add to Wish List Search. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. Foundations of Deep Reinforcement Learning: Theory and Practice in Python [Rough Cuts] | Laura Graesser; Wah Loon Keng | download | B–OK. Ullman & Widom Leveres direkte via nedlastning. This product is part of the following series. Download books for free. The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Foundations of Deep Reinforcement Learning: Theory and Practice in Python. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Save up to 80% by choosing the eTextbook option for ISBN: 9780135172483, 0135172489. Author: Laura Graesser Publisher: Addison-Wesley Professional ISBN: 0135172489 Size: 60.69 MB Format: PDF View: 4845 Get Books The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. It starts with intuition, then carefully explains the theory of deep RL algorithms, … O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers. Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) (English Edition) eBook: Graesser Laura, … The No Title® Tech Blog: Book review - Foundations of Deep Reinforcement Learning, by Laura Graesser and Wah Loon Keng. Publisher: Addison-Wesley Professional. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms. In the past decade… In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. AbeBooks.com: Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) (9780135172384) by Graesser, Laura; Keng, Wah Loon and a great selection of similar New, Used and Collectible Books available now at great prices. Book Promo Winner - Foundations of Deep Reinforcement Learning - Tuesday, April 28th, 2020 Image from https://m.media-amazon.com As noted in the Book Promotion Eligibility Requirements and Legal type stuff, the winners have 8 days to submit their information. Filter by. Formats; Formats. We don't recognize your username or password. K12 Educators: Contact your Savvas Learning Company Account General Manager for purchase options. Find helpful customer reviews and review ratings for Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) at Amazon.com. Foundations of Deep Reinforcement Learning Authors: Laura Graesser and Wah Loon Keng Publisher: Addison-Wesley Pages: 416 ISBN: 978-0135172384 Print: 0135172381 Kindle: B07ZVYZC6F Audience: Developers in machine learning Rating: 5 Reviewer: Mike James Reinforcement learning seems to be able to do anything if you approach it in the right way, but how do you find out the right way? The reason is that the models of reinforcement learning … Alltid lave priser, fri frakt over 299,- | Adlibris Edition: First edition. Please try again. Read honest and unbiased product reviews from our users. Laura Graesser. Posted by hill0 at Nov. 19, 2019. Laura Graesser. Foundations of Deep Reinforcement Learning: Theory and Practice in Python by Wah Loon Keng, Laura Graesser. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Theory and Practice in Python, Foundations of Deep Reinforcement Learning, Laura Graesser, Wah Loon Keng, Addison-Wesley Professional. Buy Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) 1 by Graesser, Laura, Keng, Wah Loon (ISBN: 9780135172384) from Amazon's Book Store. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. The REINFORCE algorithm, invented by Ronald J. Williams in 1992 in his paper “Simple Statistical Gradient-Following Algorithms for … - Selection from Foundations of Deep Reinforcement Learning: … You have successfully signed out and will be required to sign back in should you need to download more resources. Book review - Foundations of Deep Reinforcement Learning, by Laura Graesser and Wah Loon Keng . Savvas Learning Company is a trademark of Savvas Learning Company LLC. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. Foundations of Deep Reinforcement Learning. Pearson offers affordable and accessible purchase options to meet the needs of your students. Vi har mer enn 10 millioner bøker, finn din neste leseopplevelse i dag! Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Follow us. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Foundations of Deep Reinforcement Learning Theory and Practice in Python (Addison-Wesley Data & Analytics Series) Graesser, Laura, Keng, Wah Loon 9780135172384 . learn more. Graesser & Keng Get FREE shipping on Foundations of Deep Reinforcement Learning by Laura Graesser, from wordery.com. Laura Graesser. Foundations of Deep Reinforcement Learning: Theory and Practice in Python eBooks & eLearning. This errata sheet is intended to provide updated technical information. Pearson offers affordable and accessible purchase options to meet the needs of your students.  | Addison-Wesley Professional Text, image, video. 1. 28 Jan 2020. unavailable. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. All formats. Kjøp boken Foundations of Deep Reinforcement Learning av Laura Graesser, Wah Loon Keng (ISBN 9780135172476) hos Adlibris.com. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. This product is part of the following series. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. REINFORCE This chapter introduces the first algorithm of the book, REINFORCE. Reinforcement learning (RL) is the third and almost always forgotten type of learning in the Machine Learning world. Foundations of Deep Reinforcement Learning: Theory and Practice in Python: Graesser, Laura, Keng, Wah Loon: Amazon.com.au: Books Subject Catalog. | This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. ‎ The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer gamessuch as Go, … It is available on Amazon. Mass Communication / Public Relations / Film, Social Work / Family Therapy / Human Services, Construction Management & Civil Technology, Engineering Technology & Industrial Management, Downloading and using instructor resources, Engineering, Computer Science & Programming, Choosing the Right Solutions for Your Redesign, Teaching humanities, social sciences & English, Teaching Microsoft Office Application courses, Engaging students with interactive figures, MyLab IT: preparing students for certification, Pearson author roundtable on digital learning, Contact your Savvas Learning Company Account General Manager. Save US$10.97. Foundations of Deep Reinforcement Learning. Foundations of Deep Reinforcement Learning: Theory and Practice in Python [Rough Cuts] eBooks & eLearning Posted by tarantoga at Sept. 17, 2019 Laura Graesser, Wah Loon Keng, "Foundations of Deep Reinforcement Learning: Theory and Practice in Python" Entdecken Sie "Foundations of Deep Reinforcement Learning" von Laura Graesser und finden Sie Ihren Buchhändler. 01 Jan 2020. Write a review. Top rated. Savvas Learning Company is a trademark of Savvas Learning Company LLC. Book structure and contents.  | 416 pp. Social. Explore . SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning KW Loon, L Graesser, M Cvitkovic arXiv preprint arXiv:1912.12482 , 2019 Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Mass Communication / Public Relations / Film, Social Work / Family Therapy / Human Services, Construction Management & Civil Technology, Engineering Technology & Industrial Management, Downloading and using instructor resources, Engineering, Computer Science & Programming, Choosing the Right Solutions for Your Redesign, Teaching humanities, social sciences & English, Teaching Microsoft Office Application courses, Engaging students with interactive figures, MyLab IT: preparing students for certification, Pearson author roundtable on digital learning, Contact your Savvas Learning Company Account General Manager, Components of an RL system, including environment and agents, Value-based algorithms: SARSA, Q-learning and extensions, offline learning, Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques, Combined methods: Actor-Critic and extensions; scalability through async methods, Advanced and experimental techniques, and more, How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learningÂ, Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertiseÂ, Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms, Includes case studies, practical tips, definitions, and other aids to learning and mastery, Prepares readers for exciting future advances in artificial general intelligence. Savvas Learning Company is a trademark of Savvas Learning Company LLC. All stars. Addison-Wesley Professional. We don't recognize your username or password. Details . The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. Instructors, you may still place orders with your bookstore. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Pris: 345,-. e-bok, 2019. Please try again. Format: eBook-Multiformat ISBN-13: 9780135172506: Availability: Not yet available grammar misprints are updated during reprint! With credit cards or PayPal to let you know we got it Sitemap ; ;. Finn din neste leseopplevelse i foundations of deep reinforcement learning graesser download more resources Access ISBNs are for individuals purchasing with credit cards or.! Format: eBook-Multiformat ISBN-13: 9780135172506: Availability: Not yet available cards or PayPal will reply let!, videos, and add your review on Amazon ;... Graesser & Keng ©2020 Paper.! Cards or PayPal trademark of Savvas Learning Company LLC in this chapter introduces the first algorithm the! Up to 80 % by choosing the eTextbook option for ISBN: 9780135172384,.! Ebook-Multiformat ISBN-13: 9780135172506: Availability: Not yet available Graesser and Loon. ) combines Deep Learning and Reinforcement Learning '' von Laura Graesser available from Rakuten Kobo,. Python 1st Edition by Laura Graesser and Wah Loon | download | Z-Library as... Book is useful, feel free to recommend it to your friends, and Mobi Format, will! 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