This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-genera...(展开全部)

This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-genera...(展开全部)

Solve the most common data engineering and analysis challenges for modern time series data. This book provides an accessible well-rounded introduction to time series in both R and Python that will have software engineers, data scientists, and researchers up and running quickly and competently to do time-related analysis in their field of interest. Author Aileen Nielsen also off...(展开全部)

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, an...(展开全部)

Solve the most common data engineering and analysis challenges for modern time series data. This book provides an accessible well-rounded introduction to time series in both R and Python that will have software engineers, data scientists, and researchers up and running quickly and competently to do time-related analysis in their field of interest. Author Aileen Nielsen also off...(展开全部)

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, an...(展开全部)

This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, w...(展开全部)

In economics, most noncooperative game theory has focused on equilibrium in games, especially Nash equilibrium and its refinements. The traditional explanation for when and why equilibrium arises is that it results from analysis and introspection by the players in a situation where the rules of the game, the rationality of the players, and the players' payoff functions are all ...(展开全部)

作者简介： 詹姆斯·保罗·吉，美国著名语言学学者，从事心理语言学、话语分析、社会语言学、教育语言学和阅读素养研究。斯坦福大学语言学博士，亚利桑那州立大学阅读研究教授，也是威斯康星-麦迪逊大学“游戏、学习与社会”研究团队以及美国国家教育研究院的成员。近年来，他主要关注集中体现在电子游戏中以视觉符号、图像、多媒介素养为内涵的“新阅读素养”如何被习得的课题，文化学习中的“认同”理论是其学术贡献中的一大亮点。主要学术著作：《游戏改变学习：游戏素养、批判性思维与未来教育》、《数字化时代的语言和学习》、《社会性的大脑：语言、意识形态和社会实践》等。 ==================================== 译者简介： 孙静，南开大学与美国弗吉尼亚大学联合培养博士，游戏文化及功能游戏等学术专家，曾受邀担任全球电子游戏研究年会英文学术论文审稿人、组委...(展开全部)

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.