Java 9 Modularity Unveiled Crafting Scalable Applications
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Java 9 Modularity Unveiled: Crafting Scalable Applications
Dive into the nuanced world of Java 9 modularity with our comprehensive guide, "Java 9 Modularity Unveiled: Crafting Scalable Applications." This indispensable resource is crafted for developers and architects aiming to master the modular system introduced in Java 9, offering a detailed exploration of creating, maintaining, and evolving modular Java applications. From setting up a modular environment and understanding module declarations to advanced techniques and migration strategies, this book covers all the essential topics. Each chapter unfolds with examples, practical scenarios, and in-depth analysis to transform theory into actionable insights, making complex concepts accessible. Whether you're aiming to upgrade existing applications or build efficient new systems, this book is your go-to roadmap for leveraging Java’s modularity features to construct scalable, maintainable, and high-performing applications. Embrace modularity to enhance code readability, improve system agility, and stay ahead in the evolving landscape of Java development.
Reinforcement Learning
Author: Richard S. Sutton
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.