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Author: Micheal Lanham
Publisher: Packt Publishing Ltd
ISBN: 1788998766
Category : Computers
Languages : en
Pages : 379
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Book Description
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.
Author: Micheal Lanham
Publisher: Packt Publishing Ltd
ISBN: 1788998766
Category : Computers
Languages : en
Pages : 379
Get Book Here
Book Description
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.
Author: Micheal Lanham
Publisher: Packt Publishing Ltd
ISBN: 1839216778
Category : Computers
Languages : en
Pages : 420
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Book Description
Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
Author: Maxim Lapan
Publisher: Packt Publishing Ltd
ISBN: 1835882714
Category : Computers
Languages : en
Pages : 717
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Book Description
Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methods Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation Develop deep RL models, improve their stability, and efficiently solve complex environments New content on RL from human feedback (RLHF), MuZero, and transformers Book Description Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion What you will learn Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG Implement RL algorithms using PyTorch and modern RL libraries Build and train deep Q-networks to solve complex tasks in Atari environments Speed up RL models using algorithmic and engineering approaches Leverage advanced techniques like proximal policy optimization (PPO) for more stable training Who this book is for This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it’s also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance
Author: Kevin Ferguson
Publisher: Simon and Schuster
ISBN: 1638354014
Category : Computers
Languages : en
Pages : 611
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Book Description
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
Author: Dale Lane
Publisher: No Starch Press
ISBN: 1718500572
Category : Computers
Languages : en
Pages : 290
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Book Description
A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Jay Dawani
Publisher: Packt Publishing Ltd
ISBN: 183864184X
Category : Computers
Languages : en
Pages : 347
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Book Description
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Author: Maxim Lapan
Publisher: Packt Publishing Ltd
ISBN: 1838820043
Category : Computers
Languages : en
Pages : 827
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Book Description
Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms Book DescriptionDeep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.What you will learn Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft s TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik s Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques, including noisy networks and network distillation techniques Who this book is for Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL
Author: Sudharsan Ravichandiran
Publisher: Packt Publishing Ltd
ISBN: 1789344514
Category : Computers
Languages : en
Pages : 498
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Book Description
Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key FeaturesGet up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithmsImplement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook Description Deep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into RNNs and LSTM and how to generate song lyrics with RNN. Next, you will master the math for convolutional and capsule networks, widely used for image recognition tasks. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterward, you will explore various GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects. What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is for If you are a machine learning engineer, data scientist, AI developer, or simply want to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find the book very helpful.
Author: Sudharsan Ravichandiran
Publisher: Packt Publishing Ltd
ISBN: 178883691X
Category : Computers
Languages : en
Pages : 309
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Book Description
A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. What you will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman’s optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN Who this book is for If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
