Learn OpenGL
Learn OpenGL
An offline transcript of learnopengl.com
Joey de Vries
jMonkeyEngine 3.0 Beginner's Guide
jMonkeyEngine 3.0 Beginner's Guide
Fundamentals of Linear Algebra and Optimization 2017
Fundamentals of Linear Algebra
and Optimization. Jean Gallier
Algebra, Topology, Differential Calculus and Optimization for CS 2017
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 2nd 2017 6月版
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
Pro JavaFX 8 - A Definitive Guide to Building Desktop, Mobile
Learn JavaFX 8: Building User Experience and Interfaces with Java 8
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 in Action
Kotlin Android官方支持的JVM语言。
[PRML]Pattern Recognition and Machine Learning-solutions习题解答
[PRML]Pattern Recognition and Machine Learning-solutions习题解答
Machine Learning A Probabilistic Perspective Kevin P. Murphy 精校版
Machine learning A Probabilistic Perspective Kevin P. Murphy 精校版
[PRML]Pattern Recognition and Machine Learning 下载
最最经典的机器学习书籍![PRML]Pattern Recognition and Machine Learning
Advanced Lectures on Machine Learning. Springer
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 2nd
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
Introduction to Statistical Learning Theory
Deep Learning 2016
Deep Learning 2016
Ian Goodfellow, Yoshua Bengio, Aaron Courville
Reinforcement Learning state of the art
Reinforcement Learning state of the art 2016
Reinforcement Learning - An Introduction (原版,非HTML打印)
Reinforcement Learning - An Introduction (原版,非HTML打印)
Reinforcement Learning - An Introduction 2nd 2017
Reinforcement Learning - An Introduction 2nd 2017
Building a 3D Game with LibGDX
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.
Manning | Go in Action 2016
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
Foundations of Fuzzy Logic and Semantic Web Languages
Probability Theory: The Logic of Science 1st Edition
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
Manning.Git.in.Practice.2014
Netty in Action
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
Programming in Scala 2nd Edition
Learning Concurrent Programming in Scala
Learning Concurrent Programming in Scala
Atomic Scala
Atomic Scala
Calculus 4th Edition by Robert T Smith, Roland Minton
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
Cyber-Physical Systems A Computational Perspective
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
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
Functional Programming in Scala正式版
Stochastic Optimization
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
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
Measure Theory and Probability Theory
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
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)
Reinforcement Learning - An Introduction 2nd (final draft Nov 5 2017)
Algorithms for reinforcement learning
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 Insights
OpenGL_4.0_Shading_Language_Cookbook
OpenGL_4.0_Shading_Language_Cookbook