wuyamonyx的专栏

Learn OpenGL An offline transcript of learnopengl.com Joey de Vries

jMonkeyEngine 3.0 Beginner's Guide

Fundamentals of Linear Algebra and Optimization. Jean Gallier

Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Engineering Jean Gallier and Jocelyn Quaintance Book in Progress, Approx. 1440 pages (2017) http://www.cis.upenn.edu/~jean/gbooks/geomath.html

Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto Second Edition, in progress MIT Press, Cambridge, MA, 2017

Pro JavaFX 8 - A Definitive Guide to Building Desktop, Mobile

Learn JavaFX 8 shows you how to start developing rich-client desktop applications using your Java skills and provides comprehensive coverage of JavaFX 8's features. Each chapter starts with an introduction to the topic at hand, followed by a step-by-step discussion of the topic with small snippets of code. The book contains numerous figures aiding readers in visualizing the GUI that is built at every step in the discussion.

Kotlin Android官方支持的JVM语言。

[PRML]Pattern Recognition and Machine Learning-solutions习题解答

Machine learning A Probabilistic Perspective Kevin P. Murphy 精校版

最最经典的机器学习书籍！[PRML]Pattern Recognition and Machine Learning

Advanced Lectureson Machine Learning汇集了很多机器学习方面优秀的论文。 Springer出版

Neural Networks:Tricks of the Trade This book is an outgrowth of a 1996 NIPS workshop called Tricks of the Trade whose goal was to begin the process of gathering and documenting these tricks. The interest that the workshop generated, motivated us to expand our collection and compile it into this book. Although we have no doubt that there are many tricks we have missed, we hope that what we have included will prove to be useful, particularly to those who are relatively new to the field. Each chapter contains one or more tricks presented by a given author (or authors). We have attempted to group related chapters into sections, though we recognize that the different sections are far from disjoint. Some of the chapters (e.g. 1,13,17) contain entire systems of tricks that are far more general than the category they have been placed in.

Introduction to Statistical Learning Theory

Deep Learning 2016 Ian Goodfellow, Yoshua Bengio, Aaron Courville

Automated Planning theory and practice

Reinforcement Learning state of the art 2016

Reinforcement Learning - An Introduction （原版，非HTML打印）

Reinforcement Learning - An Introduction 2nd 2017

Building a 3D Game with LibGDX Paperback – August 29, 2016 by Sebastian Di Giuseppe (Author), Andreas Kruhlmann (Author), Elmar van Rijnswou (Author) Key Features Implement an exhaustive list of features that LibGDX unleashes to build your 3D game. Write, test, and debug your application on your desktop and deploy them on multiple platforms. Gain a clear understanding of the physics behind LibGDX and libraries like OpenGL and WebGL that make up LibGDX. Book Description LibGDX is a hugely popular open source, cross-platform, Java-based game development framework built for the demands of cross-platform game development. This book will teach readers how the LibGDX framework uses its 3D rendering API with the OpenGL wrapper, in combination with Bullet Physics, 3D Particles, and Shaders to develop and deploy a game application to different platforms You will start off with the basic Intellij environment, workflow and set up a LibGDX project with necessary APIs for 3D development. You will then go through LibGDX’s 3D rendering API main features and talk about the camera used for 3D. Followed by which you will build a basic 3D game with Shapes, including Basic gameplay mechanics and Basic UI. Next you will go through Modeling, Rigging, and Animation in Blender. The book will then talk about refining mechanics, new input implementations, implementing Enemy 3D models, mechanics, and gameplay balancing. The later part of the book will help you to manage secondary resources like audio, music and add 3D particles in the game to make the game more realistic. You will finally test and deploy the app on different platforms. What you will learn Setup libgdx project on Intellij IDEA Explore the perspective Camera used in the game. Learn basic 3D mechanics Bullet Physics API, Scene2D and implementing 3D shapes Load and managing game assets Implement 3D models with animations, bones (rigs), and textures. Manage and implement sound effects and Background music. Recognize, test and deploy the game across platforms. About the Author Sebastian Di Giuseppe started back in 2011 with Java Game Development and Native Android Development. With a huge passion, he spent a lot of time learning the different areas of game development, exploring on programming areas, and creating prototypes of all kinds for several platforms. With a good plan on improvement while having a full time job as an Android Developer, he also spends a lot of time on the forum java-gaming.org learning and making contacts. He joined forces with a graphic designer and a musician to peruse more professional tasks, and updates on their work which led him to meet a team of developers called who called themselves Deeep Games. With them, he made a step up and also learned Project and Product Management. With time, he joined and consulted other game development teams on management and processes. Seba now works as a full time Project and Product Manager and you can see him hangout on the Indie Game Developers facebook group posting updates on prototypes, ideas, or recruiting for future projects. You can follow Seba in his LinkedIn profile at https://ar.linkedin.com/in/sebadigiuseppe or his facebook profile at https://www.facebook.com/sebastian.digiuseppe.54.

Go in Action William Kennedy with Brian Ketelsen and Erik St. Martin Foreword by Steve Francia November 2015 ISBN 9781617291784 264 pages printed in black & white Go in Action introduces the Go language, guiding you from inquisitive developer to Go guru. The book begins by introducing the unique features and concepts of Go. Then, you'll get hands-on experience writing real-world applications including websites and network servers, as well as techniques to manipulate and convert data at speeds that will make your friends jealous.

Foundations of Fuzzy Logic and Semantic Web Languages

Probability Theory: The Logic of Science 1st Edition by E. T. Jaynes (Author), G. Larry Bretthorst (Editor) Hardcover: 753 pages Publisher: Cambridge University Press; 1 edition (June 9, 2003) Language: English ISBN-10: 0521592712 ISBN-13: 978-0521592710

Manning.Git.in.Practice.2014

Network applications must handle events intelligently and efficiently, establishing priorities, resolving conflicts, and managing resources to avoid blocks, dropouts, and the other jams that occur in high-traffic environments. Netty is a Java-based networking framework designed to handle asynchronous network events smoothly so your applications are easy to write and maintain. The framework hides all the boilerplate and low-level code from you, making it possible to keep your business-logic separate and reusable, even in different network transports and protocols. Netty has built-in support for many protocols i.e. HTTP, SPDY, and WebSockets., Netty in Action introduces the Netty framework and shows you how to incorporate it into your Java network applications. You'll learn to write highly-scalable applications without the need to dive into the low-level non-blocking APIs at the core of Java. You'll learn how to think in an asynchronous way as you work through numerous hands-on examples. You'll follow numerous examples that show you how to use Netty while you master the best practices of large-scale network apps.

Programming in Scala 2nd Edition

Learning Concurrent Programming in Scala

Atomic Scala

微积分，作者：Robert T Smith, Roland Minton Publisher: McGraw-Hill Education; 4 edition (March 11, 2011) Language: English ISBN-10: 0073383112 ISBN-13: 978-0073383118

In cyber-physical systems (CPS), sensors and embedded systems are networked together to monitor and manage a range of physical processes through a continuous feedback system. This allows distributed computing using wireless devices. Cyber-Physical Systems—A Computational Perspective examines various developments of CPS that are impacting our daily lives and sets the stage for future directions in this domain. The book is divided into six sections. The first section covers the physical infrastructure required for CPS, including sensor networks and embedded systems. The second section addresses energy issues in CPS with the use of supercapacitors and reliability assessment. In the third section, the contributors describe the modeling of CPS as a network of robots and explore issues regarding the design of CPS. The fourth section focuses on the impact of ubiquitous computing and cloud computing in CPS and the fifth section discusses security and privacy issues in CPS. The final section covers the role of CPS in big data analytics, social network analysis, and healthcare. As CPS are becoming more complex, pervasive, personalized, and dependable, they are moving beyond niche laboratories to real-life application areas, such as robotics, smart grids, green computing, and healthcare. This book provides you with a guide to current CPS research and development that will contribute to a "smarter" planet.

Cyber-Physical Systems: From Theory to Practice provides state-of-the-art research results and reports on emerging trends related to the science, technology, and engineering of CPS, including system architecture, development, modeling, simulation, security, privacy, trust, and energy efficiency. It presents the research results of esteemed professionals on cutting-edge advances in cyber-physical systems that include communications, computing, and control. The book consists of eight sections, each containing chapters contributed by leading experts in the field. Each section covers a different area that impacts the design, modeling, and evaluation of CPS, including: Control systems Modeling and design Communications and signal processing Mobility issues Architecture Security issues Sensors and applications Computing issues

Functional Programming in Scala正式版

Stochastic Optimization Authors: Johannes Josef SchneiderScott Kirkpatrick The search for optimal solutions pervades our daily lives. From the scientific point of view, optimization procedures play an eminent role whenever exact solutions to a given problem are not at hand or a compromise has to be sought, e.g. to obtain a sufficiently accurate solution within a given amount of time. This book addresses stochastic optimization procedures in a broad manner, giving an overview of the most relevant optimization philosophies in the first part. The second part deals with benchmark problems in depth, by applying in sequence a selection of optimization procedures to them. While having primarily scientists and students from the physical and engineering sciences in mind, this book addresses the larger community of all those wishing to learn about stochastic optimization techniques and how to use them.

Decision Making Under Uncertainty Theory and Application. 2015 By Mykel J. Kochenderfer With Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre and John Vian Overview Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical enginee

Authors Krishna B. Athreya Soumendra N. Lahiri Copyright 2006 Publisher Springer-Verlag New York DOI 10.1007/978-0-387-35434-7 This is a graduate level textbook on measure theory and probability theory. The book can be used as a text for a two semester sequence of courses in measure theory and probability theory, with an option to include supplemental material on stochastic processes and special topics. It is intended primarily for first year Ph.D. students in mathematics and statistics although mathematically advanced students from engineering and economics would also find the book useful. Prerequisites are kept to the minimal level of an understanding of basic real analysis concepts such as limits, continuity, differentiability, Riemann integration, and convergence of sequences and series. A review of this material is included in the appendix.

Statistical Reinforcement Learning: Modern Machine Learning Approaches Masashi Sugiyama Taylor & Francis, 16 Mar 2015 - Business & Economics - 206 pages Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspective Lays out the associated optimization problems for each reinforcement learning scenario covered Provides thought-provoking statistical treatment of reinforcement learning algorithms The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

Reinforcement Learning - An Introduction 2nd (final draft Nov 5 2017)

主要责任者 Szepesvári, Csaba. 题名 Algorithms for reinforcement learning [electronic resource] / Csaba Szepesvári. 出版资料 San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2010. 摘要附注 Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

OpenGL Insights

OpenGL_4.0_Shading_Language_Cookbook