Randomizer

Hey! Listen!

How does analyzing video games as hypertexts expand the landscape of research for video game rhetoricians and games studies scholars? This is the first book to focus on how hypertext rhetoric impacts the five canons of rhetoric, and to apply that hypertext rhetoric to the study of video games. It also explores how ludonarrative agency is seized by players seeking to express themselves in ways that game makers did not necessarily intend when making the games that players around the world enjoy. This book takes inspiration from The Legend of Zelda, a series which players all over the world have spent decades deconstructing through online playthroughs, speedruns, and glitch hunts. Through these playthroughs, players demonstrate their ability to craft their own agency, independent of the objectives built by the makers of these games, creating new rhetorical situations worthy of analysis and consideration.

Practical Simulations for Machine Learning

Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits

JAVA 精典範例,第二版