{"product_id":"138151","title":"Deep Learning from Scratch 4 ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCfJfoP9wfAQSYu86CUe72Pp6hf.png?v=1765060751\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Deep Learning from Scratch 4 \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/124640233\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eThe \"Deep Learning from Scratch\" series, this time focusing on reinforcement learning!\u003cbr\u003e From core reinforcement learning theory to problem solving and deep reinforcement learning, all in one book!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eThe characteristic of this book, as the title suggests, is ‘building from the ground up.’\u003cbr\u003e Learn by implementing reinforcement learning algorithms from scratch, without relying on external libraries whose internals are unknown.\u003cbr\u003e Understand the principles through pictures, solve reinforcement learning problems with math, and then implement them with code to review what you've learned.\u003cbr\u003e The code is written to be as concise as possible while clearly revealing the important ideas in reinforcement learning.\u003cbr\u003e It is structured so that you can experience both the difficulty and the fun of reinforcement learning by gradually increasing the level and tackling various problems.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eCHAPTER 1: THE BANDIT PROBLEM\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _1.1 Machine Learning Classification and Reinforcement Learning\u003cbr\u003e _1.2 Bandit Issue\u003cbr\u003e _1.3 Bandit Algorithm\u003cbr\u003e _1.4 Bandit Algorithm Implementation\u003cbr\u003e _1.5 Abnormal Issue\u003cbr\u003e _1.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2 Markov Decision Processes\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e_2.1 What is a Markov Decision Process (MDP)?\u003cbr\u003e _2.2 Environment and agent as formulas\u003cbr\u003e _2.3 MDP Goals\u003cbr\u003e _2.4 MDP Example\u003cbr\u003e _2.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 3 Bellman Equation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _3.1 Derivation of Bellman equation\u003cbr\u003e _3.2 Example of Bellman Equation\u003cbr\u003e _3.3 Action-Value Function (Q Function) and Bellman Equation\u003cbr\u003e _3.4 Bellman optimality equation\u003cbr\u003e _3.5 Example of Bellman Optimality Equation\u003cbr\u003e _3.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 4 DYNAMIC PROGRAMMING\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _4.1 Dynamic Programming and Policy Evaluation\u003cbr\u003e _4.2 Towards a bigger problem\u003cbr\u003e _4.3 Policy Repetition Method\u003cbr\u003e _4.4 Implementing Policy Iteration\u003cbr\u003e _4.5 Value Repetition Method\u003cbr\u003e _4.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5 Monte Carlo Method\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _5.1 Monte Carlo Method Basics\u003cbr\u003e _5.2 Evaluating Policies Using the Monte Carlo Method\u003cbr\u003e _5.3 Monte Carlo method implementation\u003cbr\u003e _5.4 Controlling Policy Using Monte Carlo Methods\u003cbr\u003e _5.5 Off-Policy and Importance Sampling\u003cbr\u003e _5.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6 TD Law\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _6.1 Evaluating policies using the TD method\u003cbr\u003e _6.2 SARSA\u003cbr\u003e _6.3 Off-Policy SARSA\u003cbr\u003e _6.4 Q Running\u003cbr\u003e _6.5 Distribution Model and Sample Model\u003cbr\u003e _6.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7: Neural Networks and Q-Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _7.1 DeZero Basics\u003cbr\u003e _7.2 Linear Regression \u003cbr\u003e_7.3 Neural Networks\u003cbr\u003e _7.4 Q-Learning and Neural Networks\u003cbr\u003e _7.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 DQN\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _8.1 OpenAI Gym\u003cbr\u003e _8.2 DQN's core technology\u003cbr\u003e _8.3 DQN and Atari\u003cbr\u003e _8.4 DQN Extension\u003cbr\u003e _8.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 9 POLICY GRADIENTS\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _9.1 The simplest policy gradient method\u003cbr\u003e _9.2 REINFORCE\u003cbr\u003e _9.3 Baseline\u003cbr\u003e _9.4 Actor-Critic\u003cbr\u003e _9.5 Advantages of Policy-Based Techniques\u003cbr\u003e _9.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 10 One Step Further\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _10.1 Classification of Deep Reinforcement Learning Algorithms\u003cbr\u003e _10.2 Advanced Algorithms of the Policy Gradient Series\u003cbr\u003e _10.3 Advanced algorithms of the DQN series\u003cbr\u003e _10.4 Case Study\u003cbr\u003e _10.5 Challenges and Potentials of Deep Reinforcement Learning\u003cbr\u003e _10.6 Summary\u003cbr\u003e\u003cbr\u003e APPENDIX A Off-Policy Monte Carlo Method\u003cbr\u003e A.1 Off-policy Monte Carlo method theory\u003cbr\u003e A.2 Implementation of the Off-Policy Monte Carlo Method\u003cbr\u003e\u003cbr\u003e APPENDIX B n-step TD method\u003cbr\u003e\u003cbr\u003e APPENDIX C Understanding Double DQN\u003cbr\u003e C.1 What is overfitting in DQN?\u003cbr\u003e C.2 Overfitting Solutions\u003cbr\u003e\u003cbr\u003e APPENDIX D Policy Gradient Proof\u003cbr\u003e D.1 Deriving the policy slope method\u003cbr\u003e D.2 Baseline Derivation\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4414\/MidCate003\/441326102.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e  \u003cdiv\u003e\n\u003cb\u003eThe shortcut to mastering reinforcement learning is to build a solid foundation!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Deep learning is a very hot field, with new algorithms and applications being announced almost every day.\u003cbr\u003e With the rapid pace of development, related technologies and services are also evolving rapidly, so things that were once popular are now disappearing.\u003cbr\u003e But on the other hand, there are things that have been passed down without change.\u003cbr\u003e The knowledge you learn in this book is that which does not change.\u003cbr\u003e The ideas and techniques that form the basis of reinforcement learning remain unchanged.\u003cbr\u003e Even modern algorithms are based on ideas that have existed for a long time.\u003cbr\u003e Topics such as the fundamentals of reinforcement learning, Markov decision processes, the Bellman equation, Q-learning, and neural networks will continue to be important. \u003cbr\u003eTherefore, to understand current reinforcement learning and even deep reinforcement learning, it is actually a shortcut to learn the basics of reinforcement learning step by step.\u003cbr\u003e We will kindly explain each formula symbol and each line of code so that you can read it even if you have only basic knowledge of Python and mathematics.\u003cbr\u003e I hope you will learn the basics of reinforcement learning through this book and experience the beauty of \"what doesn't change.\"\u003cbr\u003e May the Force be with you all…\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Readers who want to properly learn the principles of reinforcement learning\u003cbr\u003e - Developers who want to understand deep learning more deeply\u003cbr\u003e - Beginners in data science who have knowledge of Python and are interested in deep learning and reinforcement learning.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eKey Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Solving the 'bandit problem', which sequentially finds the best candidate among several candidates (Chapter 1)\u003cbr\u003e - Defining a general reinforcement learning problem as a 'Markov decision process' (Chapter 2) \u003cbr\u003e- Deriving the 'Bellman equation', which is key to finding the optimal answer in the Markov decision process (Chapter 3)\u003cbr\u003e - Methods for solving the Bellman equation: dynamic programming (Chapter 4), Monte Carlo method (Chapter 5), and TD method (Chapter 6).\u003cbr\u003e - Learn about deep learning and apply it to reinforcement learning algorithms (Chapter 7)\u003cbr\u003e - Learn how to implement and extend DQN (Chapter 8)\u003cbr\u003e A Different Approach from DQN: The Policy Gradient Algorithm (Chapter 9)\u003cbr\u003e - A3C\/DDPG\/TRPO\/Rainbow Algorithm and Deep Reinforcement Learning (Chapter 10) \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e January 26, 2024\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 372 pages | 684g | 183*235*15mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169211956\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 116921195X \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893204549674,"sku":"138151","price":38.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/08bb159a68bb5e7e849f2bfc9379b189.jpg?v=1765391885","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138151","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}