Download or read book Insight of DeepMind Learning written by MD Sadique Shaikh and published by Eliva Press. This book was released on 2020-12-26 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning has rapidly become one of the hottest research areas in the deep learning ecosystem. The fascination with reinforcement learning is related to the fact that, from all the deep learning modalities, is the one that resemble the most how humans learn. In the last few years, no company in the world has done more to advance the stage of deep reinforcement learning than Alphabet's subsidiary DeepMind. Since the launch of its famous AlphaGo agent, DeepMind has been at the forefront of reinforcement learning research. A few days ago, they published a new research that attempts to tackle one of the most challenging aspects of reinforcement learning solutions: multi-tasking. Since we are infants, multi-tasking becomes an intrinsic element of our cognition. The ability to performing and learning similar tasks concurrently is essential to the development of the human mind. From the neuroscientific standpoint, multi-tasking remains largely a mystery and that, not surprisingly, we have had a heck of hard time implementing artificial intelligence (AI) agents that can efficiently learn multiple domains without requiring a disproportional amount of resources. This challenge is even more evident in the case of deep reinforcement learning models that are based on trial and error exercises which can easily cross the boundaries of a single domain. Biologically speaking, you can argue that all learning is a multi-tasking exercise. This monograph introduces you with DeepMind learning.
Download or read book Deep Insight A Practical Guide to Unlocking the Power O written by William Celajes, Jr and published by Celajes Jr William. This book was released on 2024-03-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Insight is a comprehensive guide that introduces readers to the revolutionary technology of DeepMind. Authored by industry experts, the book provides practical strategies for unlocking the immense power of DeepMind. DeepMind is at the forefront of artificial intelligence and has achieved groundbreaking milestones in the field. The book explores the origins of DeepMind and its contributions to machine learning, showcasing its ability to tackle complex tasks and enhance human capabilities. The book is divided into several sections, starting with an introduction to the fundamentals of DeepMind. It explains the historical development of the technology, making it accessible to readers with varying degrees of knowledge in artificial intelligence. Deep Insight delves deeper into the core components of DeepMind, such as neural networks, computer vision, and natural language processing. Real-life examples, case studies, and practical applications are used to illustrate the democratizing impact of DeepMind across industries, from healthcare to logistics and finance. In addition to exploring the possibilities offered by DeepMind, the book also addresses important ethical considerations and limitations of the technology. It guides readers in responsible and conscientious use of DeepMind, highlighting the ethical implications of AI in society. What sets Deep Insight apart is its focus on practicality. The book offers hands-on methodologies, strategies, and best practices that maximize DeepMind's potential. Through tutorials, exercises, and step-by-step guidance, readers are empowered to apply DeepMind principles in various domains, regardless of their background. Deep Insight also provides an exclusive look into DeepMind's latest innovations and ongoing research. It covers trending topics like reinforcement learning, unsupervised learning, generative modeling, and explainable AI, offering readers the opportunity to explore uncharted territories in DeepMind. Written by a team of industry pioneers, academic visionaries, and thought leaders in collaboration with DeepMind experts, this book offers a well-rounded perspective on the technology and its implications. Each chapter incorporates the wisdom and expertise of those who have made significant contributions to the development of DeepMind. Deep Insight invites readers to embark on a profound journey of exploration, unraveling the mysteries and unlocking the transformative potential of DeepMind. It encourages readers to unleash their creativity, align themselves with thought leaders, and embrace the infinite possibilities brought forth by DeepMind. Whether seeking actionable insights for business growth, knowledge acquisition, or technological advancements, Deep Insight empowers readers to harness the power of DeepMind and revolutionize their journey in artificial intelligence. With this practical guide as a navigational tool, readers can achieve deep insights and unlock a world of endless possibilities in the breathtaking realm of DeepMind.
Download or read book Deep Reinforcement Learning written by Mohit Sewak and published by Springer. This book was released on 2019-06-27 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. 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.
Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book Grokking Deep Learning written by Andrew W. Trask and published by Simon and Schuster. This book was released on 2019-01-23 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Download or read book The Deep Learning AI Playbook written by Carlos Perez and published by Lulu.com. This book was released on 2017-10-11 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Just like any new technology, what perplexes many is the question of how to apply Deep Learning in a business context. Technology that is disruptive does not automatically imply that the development of valuable use cases are apparent. For years, many people could not figure out how to monetize the World Wide Web. We are in that same situation with Deep Learning AI. The developments are mind-boggling but the monetization is far from being obvious.Deep Learning Artificial Intelligence involves the interplay of Computer Science, Physics, Biology, Linguistics and Psychology. In addition to that, it is technology that can be extremely disruptive. Furthermore, the ramifications to society and even our own humanity can be immense. There are few subjects that are as captivating and as consequential as this. Surprisingly, there is very little that is written about this new technology in a more comprehensive and cohesive way. This book is an opinionated take on the developments of Deep Learning AI.
Download or read book The DeepMind Handbook written by William Celajes, Jr and published by Celajes Jr William. This book was released on 2024-03-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The DeepMind Handbook: Strategies for Learning and Mastering AI Technologies is an expertly curated guide that explores the captivating world of artificial intelligence from the perspective of DeepMind, a leading research organization in the field. Written by a consortium of industry experts, this comprehensive book provides essential knowledge and practical strategies to navigate the complexities of AI. Over the past decade, AI has rapidly evolved and influenced various industries such as healthcare, finance, gaming, and robotics. DeepMind, renowned for its cutting-edge AI technologies, is uniquely positioned to share its expertise. The DeepMind Handbook brings together the collective insights of DeepMind researchers and engineers, enabling readers to understand the exciting potential of AI and master its applications. Regardless of your background, this handbook presents AI concepts in an accessible manner. Each chapter begins with a concise overview, building a strong foundation of core concepts. As the book progresses, it reveals the inner workings of machine learning algorithms, neural networks, reinforcement learning, natural language processing, and more. By emphasizing incremental learning, the handbook encourages readers to explore foundational concepts to develop a comprehensive understanding. With a logical structure, this handbook seamlessly integrates theory with practical applications. Real-world case studies and examples highlight the versatility of AI across industries, demonstrating its vast capabilities. The book also addresses ethics and the societal impact of AI, considering fairness, transparency, and bias. By exploring these topics, readers gain a well-rounded perspective on the responsible use of AI. In addition to theoretical knowledge, the DeepMind Handbook equips readers with practical strategies for learning and mastering AI technologies. The authors meticulously outline learning paths, necessary skill sets, resources, and tools to cultivate AI expertise. With a focus on hands-on experience, the handbook offers step-by-step tutorials, coding exercises, and access to DeepMind's exclusive learning resources. Whether your goal is to develop AI applications or simply gain a better understanding of this transformative field, this handbook provides the necessary tools for success. What distinguishes this handbook is its commitment to democratizing AI education. Through its collaborative authorship, it encompasses the collective wisdom and diverse perspectives of the DeepMind team. The content has been carefully crafted to ensure inclusivity and accessibility, catering to readers of all skill levels and backgrounds. Simplified explanations, visual aids, and glossaries accompany complex topics, fostering understanding and engagement.
Download or read book Deep Reinforcement Learning Hands On written by Maxim Lapan and published by Packt Publishing Ltd. This book was released on 2018-06-21 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Book Description Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots. What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbots Who this book is for Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
Download or read book AI Deep Mind and Reinforcement learning Basic explanation absolutely for beginners Get 3 amazing projects source code written by Vibhas Mahaldar and published by Vibhas Mahaldar. This book was released on 2021-07-19 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on Artificial Intelligence and Machine Learning in this book have 2 parts, First part is about full introduction of Artificial Intelligence and second part is about Deep Mind and Reinforcement learning. 1 part : In this part we learn about Artificial Intelligence subsets like · AI explanation · Understanding AI · Categorization of AI · Special Consideration · Applications of AI around us/Details · Understanding AI, ML, DL/Details · Introduction to AI domains/Details · AI Ethics explanation · Why are AI ethics important? · What are the ethical challenges of AI? · Game Time · Python game Stone, Paper and Scissor . SDG Goals/Details 2 part : in this part we learn about Deep Mind and Reinforcement learning and there subset like Learn Machine to walk · Explanation Alpha Go · Explanation · History · defeated Go Players Reinforcement Learning · Explanation · Bipedal Walker (Example 1) · Solving the environment · Training visualization (Slightly uneven terrain) · Training visualization (Hardcore terrain) · DDPG network architecture · Example 2 · Q Values, and Q Learning Deep Q Network · Explanation Neural Network · Biological Neural Networks · Artificial neural networks (ANNs) · Training . Snake game · Explanation · Action · State · Reward Introduction to Google Colab · Explanation · Uploading Files Code · Upload and unzip · Explanation of code . Source code And this book is absolutely for beginners and also get the 3 amazing projects source code.
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Download or read book Deep Thinking written by Garry Kasparov and published by PublicAffairs. This book was released on 2017-05-02 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Garry Kasparov's 1997 chess match against the IBM supercomputer Deep Blue was a watershed moment in the history of technology. It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.
Download or read book DeepMind Learning Machine written by Mohd. Sadique Shaikh Anwar and published by . This book was released on 2018-10-04 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Geometry of Deep Learning written by Jong Chul Ye and published by Springer Nature. This book was released on 2022-01-05 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems. Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
Download or read book Hands On Deep Learning for Games written by Micheal Lanham and published by Packt Publishing Ltd. This book was released on 2019-03-30 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key FeaturesApply the power of deep learning to complex reasoning tasks by building a Game AIExploit the most recent developments in machine learning and AI for building smart gamesImplement deep learning models and neural networks with PythonBook Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learnLearn the foundations of neural networks and deep learning.Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.Working with Unity ML-Agents toolkit and how to install, setup and run the kit.Understand core concepts of DRL and the differences between discrete and continuous action environments.Use several advanced forms of learning in various scenarios from developing agents to testing games.Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
