%{search_type} search results

• Chapter 1: Deep Learning PerceptronChapter Goal: In this chapter we introduce the basics of deep learning from the perceptron to multi-layer perceptron.No of pages: 30Sub -Topics1. Understanding deep learning and supervised learning.1. Using the perceptron for supervised learning.2. Constructing a multilayer perceptron.3. Discover the basics of activation, loss, optimization and back propagation for problems of regression and classification. Chapter 2: Unleashing Autoencoders and Generative Adversarial NetworksChapter Goal: This chapter introduces the autoencoder and GAN for simple content generation. Along the way we also learn about using convolutional network layers for better feature extraction.No of pages: 30Sub - Topics 1. Why we need autoencoders and how they function.2. Improving on the autoencoder with convolutional network layers.3. Generating content with the GAN.4. Explore methods for improving on the vanilla GAN. Chapter 3: Exploring the Latent SpaceChapter Goal: In this chapter we discover the latent space in AI. What it means to move through the AI latent space using variational autoencoders and conditional GANs.No of pages : 30 Sub - Topics: 1. Understanding variation and the variational autoencoder.2. Exploring the latent space with a VAE.3. Extending a GAN to be conditional.4. Generate interesting foods using a conditional GAN. Chapter 4: GANs, GANs and More GANsChapter Goal: In this chapter we begin uncovering the vast variations in GANs and their applications. We start with basics like the double convolution GAN and work up to the Stack and Progressive GANs.No of pages: 30Sub - Topics: 1. Look at samples from the many variations of GANs.2. Setup and use a DCGAN.3. Understand how a StackGAN works.4. Work with and use a ProGAN. Chapter 5: Image to Image Translation with GANs
• Covers: Pix2Pix and DualGAN, side projects for understanding with ResNET and UNET, advanced network architectures for image classification/generation
• Chapter 6: Translating Images with Cycle Consistency
• Covers: Cycle consistency loss and the CycleGAN, BiCycleGAN and StarGAN
• Chapter 7: Styling with GANs
• Covers: StyleGAN, Attention and the Self-attention GAN with a look at DeOldify
• Chapter 8: Developing DeepFakesChapter Goal: DeepFakes are taking the world by storm and in this chapter, we explore how to use a DeepFakes project. No of pages: 301. Learn how to isolate faces or other points of interest in images or video.2. Extract and replace faces from images or video.3. Use DeepFakes GAN to generate facial images based on input image.4. Put it all together and allow the user to generate their own DeepFake video. Chapter 9: Uncovering Adversarial Latent AutoencodersChapter Goal: GANs are not the only technique that allows for content manipulation and generations. In this chapter we look at the ALAE method for generating content.No of pages: 1. Look at how to extend autoencoders for adversarial learning.2. Understanding how AE can be used to explore the latent space in data.3. Use ALAE to generate conditional content.4. Revisit our previous foods example and see what new foods we can generate. Chapter 10: Video Content with First Order Model MotionChapter Goal: In this chapter we explore a new technique for animating static images called First Order Model Motion. At the end of this chapter we will use this technique to create avatars for Skype or Zoom.No of pages: 30 1. Discover the basic of First Order Model Motion, what it is and how it works.2. Be able to apply FOMM to a number of static image datasets for various applications.3. Use the project Avatarify for generating real-time avatars from static avatars.4. Use Avatarify real-time in applications like Zoom or Skype.
• (source: Nielsen Book Data)

The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality. In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects. By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new. What You Will Learn Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs) Explore variations of GAN Understand the basics of other forms of content generation Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2 Who This Book Is For Machine learning developers and AI enthusiasts who want to understand AI content generation techniques.
(source: Nielsen Book Data)

Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video. Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application. Learn key concepts for data science, machine learning, and deep learning Explore tools like Video AI and AutoML Tables Build a simple language processor using deep learning systems Perform image recognition using CNNs, transfer learning, and GANs Use Google's Dialogflow to create chatbots and conversational AI Analyze video with automatic video indexing, face detection, and TensorFlow Hub Build a complete working AI agent application.

• Table of Contents Understanding Rewards-Based Learning Dynamic Programming and the Bellman Equation Monte Carlo Methods Temporal Difference Learning Exploring SARSA Going Deep with DQN Going Deeper with DDQN Policy Gradient Methods Optimizing for Continuous Control All about Rainbow DQN Exploiting ML-Agents DRL Frameworks 3D Worlds From DRL to AGI.
• (source: Nielsen Book Data)

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features Get to grips with the different reinforcement and DRL algorithms for game development Learn how to implement components such as artificial agents, map and level generation, and audio generation Gain insights into cutting-edge RL research and understand how it is similar to artificial general research Book DescriptionWith 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 learn Understand how deep learning can be integrated into an RL agent Explore basic to advanced algorithms commonly used in game development Build agents that can learn and solve problems in all types of environments Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem Develop game AI agents by understanding the mechanism behind complex AI Integrate all the concepts learned into new projects or gaming agents Who this book is forIf 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.
(source: Nielsen Book Data)

• Table of Contents Understanding Rewards-Based Learning Dynamic Programming and the Bellman Equation Monte Carlo Methods Temporal Difference Learning Exploring SARSA Going Deep with DQN Going Deeper with DDQN Policy Gradient Methods Optimizing for Continuous Control All about Rainbow DQN Exploiting ML-Agents DRL Frameworks 3D Worlds From DRL to AGI.
• (source: Nielsen Book Data)

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features Get to grips with the different reinforcement and DRL algorithms for game development Learn how to implement components such as artificial agents, map and level generation, and audio generation Gain insights into cutting-edge RL research and understand how it is similar to artificial general research Book DescriptionWith 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 learn Understand how deep learning can be integrated into an RL agent Explore basic to advanced algorithms commonly used in game development Build agents that can learn and solve problems in all types of environments Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem Develop game AI agents by understanding the mechanism behind complex AI Integrate all the concepts learned into new projects or gaming agents Who this book is forIf 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.
(source: Nielsen Book Data)

• Table of Contents Deep Learning for Games Convolutional and Recurrent Networks GAN for Games Building a Deep Learning Gaming Chatbot Introducing DRL Unity ML-Agents Agent and the Environment Understanding PPO Rewards and Reinforcement Learning Imitation and Transfer Learning Building Multi-Agent Environments Debugging/Testing a Game with DRL Obstacle Tower Challenge and Beyond.
• (source: Nielsen Book Data)

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features Apply the power of deep learning to complex reasoning tasks by building a Game AI Exploit the most recent developments in machine learning and AI for building smart games Implement deep learning models and neural networks with Python Book DescriptionThe 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 learn Learn 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 forThis 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.
(source: Nielsen Book Data)

• Getting started
• Mapping the player's location
• Making the avatar
• Spawning the catch
• Catching the prey in AR
• Storing the catch
• Creating the AR world
• Interacting with an AR world
• Finishing the game
• Troubleshooting.

Create your own augmented reality games from scratch with Unity 5 About This Book * Create your own augmented reality game from scratch and join the virtual reality gaming revolution * Use the latest Unity 5 VR SDK to create pro-level AR games like Pokemon Go * Innovate and explore the latest and most promising trend of AR gaming in the mobile gaming industry Who This Book Is For This book is for those who have a basic knowledge of game development techniques, but no previous knowledge of Unity is required. Some basic programming knowledge would be desirable, but the book is an introduction to the topic. The book is also suitable for experienced developers new to GIS or GPS development. What You Will Learn * Build a location-based augmented reality game called Foodie Go * Animate a player's avatar on a map * Use the mobile device's camera as a game background * Implement database persistence with SQLLite4Unity3D to carry inventory items across game sessions * Create basic UI elements for the game, inventory, menu, and settings * Perform location and content searches against the Google Places API * Enhance the game's mood by adding visual shader effects * Extend the game by adding multiplayer networking and other enhancements In Detail The heyday of location-based augmented reality games is upon us. They have been around for a few years, but the release of Pokemon Go was a gamechanger that catalyzed the market and led to a massive surge in demand. Now is the time for novice and experienced developers alike to turn their good ideas into augmented reality (AR) mobile games and meet this demand! If you are keen to develop virtual reality games with the latest Unity 5 toolkit, then this is the book for you. The genre of location-based AR games introduces a new platform and technical challenges, but this book will help simplify those challenges and show how to maximize your game audience. This book will take you on a journey through building a location-based AR game that addresses the core technical concepts: GIS fundamentals, mobile device GPS, mapping, map textures in Unity, mobile device camera, camera textures in Unity, accessing location-based services, and other useful Unity tips. The technical material also discusses what is necessary for further development to create a multiplayer version of the game. At the end, you will be presented with troubleshooting techniques in case you get into trouble and need a little help. Style and approach This book shows you how to create every step of the game and gives practical examples.
(source: Nielsen Book Data)

• Getting started
• Mapping the player's location
• Making the avatar
• Spawning the catch
• Catching the prey in AR
• Storing the catch
• Creating the AR world
• Interacting with an AR world
• Finishing the game
• Troubleshooting.

Create your own augmented reality games from scratch with Unity 5 About This Book * Create your own augmented reality game from scratch and join the virtual reality gaming revolution * Use the latest Unity 5 VR SDK to create pro-level AR games like Pokemon Go * Innovate and explore the latest and most promising trend of AR gaming in the mobile gaming industry Who This Book Is For This book is for those who have a basic knowledge of game development techniques, but no previous knowledge of Unity is required. Some basic programming knowledge would be desirable, but the book is an introduction to the topic. The book is also suitable for experienced developers new to GIS or GPS development. What You Will Learn * Build a location-based augmented reality game called Foodie Go * Animate a player's avatar on a map * Use the mobile device's camera as a game background * Implement database persistence with SQLLite4Unity3D to carry inventory items across game sessions * Create basic UI elements for the game, inventory, menu, and settings * Perform location and content searches against the Google Places API * Enhance the game's mood by adding visual shader effects * Extend the game by adding multiplayer networking and other enhancements In Detail The heyday of location-based augmented reality games is upon us. They have been around for a few years, but the release of Pokemon Go was a gamechanger that catalyzed the market and led to a massive surge in demand. Now is the time for novice and experienced developers alike to turn their good ideas into augmented reality (AR) mobile games and meet this demand! If you are keen to develop virtual reality games with the latest Unity 5 toolkit, then this is the book for you. The genre of location-based AR games introduces a new platform and technical challenges, but this book will help simplify those challenges and show how to maximize your game audience. This book will take you on a journey through building a location-based AR game that addresses the core technical concepts: GIS fundamentals, mobile device GPS, mapping, map textures in Unity, mobile device camera, camera textures in Unity, accessing location-based services, and other useful Unity tips. The technical material also discusses what is necessary for further development to create a multiplayer version of the game. At the end, you will be presented with troubleshooting techniques in case you get into trouble and need a little help. Style and approach This book shows you how to create every step of the game and gives practical examples.
(source: Nielsen Book Data)

Create 'AAA' quality game audio with new features and tools built for Unity About This Book * Explore the basics of audio development in Unity to create spatial sound, mixing, effects, composition, adaptive audio and more. * Leverage the Audio Mixer of Unity 5.x to create blockbuster sound and music for your game. * Learn about developing professional audio for games with FMOD Studio and composing original music with Reaper. * Build amazing audio synchronized graphic visualizations with Unity. * Understand how real-time character lip syncing can be implemented. Who This Book Is For The ideal target audience for this book will be game developers, both Indie as well as semi pro. No prior knowledge of Unity and audio development is assumed, What You Will Learn * Develop game audio and other audio effects with Unity * Getting familiar with the new Audio Mixer introduced in Unity 5 * Implement dynamic and adaptive audio using various tools and strategies * Explore interesting ways to incorporate audio into a game with sound visualization * Use 3rd party professional audio development tools like FMOD * Compose original music and record vocals * Understand and troubleshoot audio performance issues In Detail Game Audio is one of the key components in making a game successful and it is quite popular in the gaming industry. So if you are a game developer with an eye on capturing the gamer market then this book is the right solution for you. In this book, we will take you through a step by step journey which will teach you to implement original and engaging soundtracks and SFX with Unity 5.x. You will be firstly introduced to the basics of game audio and sound development in Unity. After going through the core topics of audio development: audio sources, spatial sound, mixing, effects, and more; you will then have the option of delving deeper into more advanced topics like dynamic and adaptive audio. You will also learn to develop dynamic and adaptive audio using the Unity Audio Mixer. Further, you will learn how professional third party tools like FMOD are used for audio development in Unity. You will then go through the creation of sound visualization techniques and creating your own original music using the simple yet powerful audio workstation Reaper. Lastly, you will go through tips, techniques and strategies to help you optimize game audio performance or troubleshoot issues. At the end of the book, you'll have gained the skills to implement professional sound and music. Along with a good base knowledge audio and music principles you can apply across a range of other game development tools. Style and approach This book will have a step by step practical approach where downloadable free games will be given with the book and readers will be free to work with them.
(source: Nielsen Book Data)

"ARアプリ開発の入門書。本書ではスマホゲー ム『Foody GO』を実際に作りながら位置情報ベースのARゲー ムについて学びます。『Foody GO』はモンスターを探して捕まえレストランに 連れていってアイテムとして売るというアドベ ンチャーゲームです。モバイル端末のGPSから現 在位置を取得しゲームの世界観に合わせたマッ プを描画してその上に自分のアバターとモンス ターをアニメーション付きで表示します。Android やiPhoneで遊べる実践的なスマホゲームを自分で 作ることができるので、読者はUnityによるARゲー ム開発と関連技術を体系的かつ体験的に学べま す。日本語版では、ARKitやTangoによるARビューの 実装についての解説を巻末付録として収録しま した。" -- Provided by publisher. "ARアプリ開発の入門書。本書ではスマホゲー ム『Foody GO』を実際に作りながら位置情報ベースのARゲー ムについて学びます。『Foody GO』はモンスターを探して捕まえレストランに 連れていってアイテムとして売るというアドベ ンチャーゲームです。モバイル端末のGPSから現 在位置を取得しゲームの世界観に合わせたマッ プを描画してその上に自分のアバターとモンス ターをアニメーション付きで表示します。Android やiPhoneで遊べる実践的なスマホゲームを自分で 作ることができるので、読者はUnityによるARゲー ム開発と関連技術を体系的かつ体験的に学べま す。日本語版では、ARKitやTangoによるARビューの 実装についての解説を巻末付録として収録しま した。" -- Provided by publisher.