下载 >  开发技术 >  C++ > C++ Essentials

C++ Essentials

C++ Essentials.pdf. A very good textbook to learn C++.
2009-06-10 上传大小:541KB
想读
分享
收藏 举报
android-studio-3-development-essentials2018

Fully updated for Android Studio 3.0 and Android 8, the goal of this book is to teach the skills necessary to develop Android based applications using the Android Studio Integrated Development Environment (IDE), the Android 8 Software Development Kit (SDK) and the Java programming language. Beginning with the basics, this book provides an outline of the steps necessary to set up an Android development and testing environment. An overview of Android Studio is included covering areas such as tool windows, the code editor and the Layout Editor tool. An introduction to the architecture of Android is followed by an in-depth look at the design of Android applications and user interfaces using the Android Studio environment. More advanced topics such as database management, content providers and intents are also covered, as are touch screen handling, gesture recognition, camera access and the playback and recording of both video and audio. This edition of the book also covers printing, transitions and cloud-based file storage.

立即下载
Computing Essentials 2017精校原版 (计算机专业英语 绝对经典)

Computing Essentials 2017精校原版 (计算机专业英语 绝对经典)

立即下载
Android Studio 3.0 Development Essentials - Android 8 Edition

本书介绍了最新的安卓8的开发。此文档是此书的前五个章节。

立即下载
SRS Audio Essentials 1.2.3 正式版已破解

音效增强(SRS Audio Essentials) 1.2.3 正式版已破解

立即下载
SPSS_Statistics_REssentials_22_win64

这是适用于spss22版本的R插件,可用于win64,亲测可用

立即下载
Android Studio 3.0 Development Essentials, Android 8 Edition 无水印转化版pdf

Android Studio 3.0 Development Essentials, Android 8 Edition 英文无水印转化版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除查看此书详细信息请在美国亚马逊官网搜索此书

立即下载
McGraw-Hill-Computing_Essentials_2017计算机专业英语,国外原版,(第一卷,共三卷)

专业英语 McGraw-Hill-Computing_Essentials_2017计算机专业英语,国外原版

立即下载
Essentials of Systems Analysis and Design

Essentials of Systems Analysis and Design sixth edition Joseph S. Valacich • Joey F. George • Jeffrey A. Hoffer ISBN 10: 1-292-07661-5 ISBN 13: 978-1-292-07661-4

立即下载
LLVM.Essentials.1785280

Become familiar with the LLVM infrastructure and start using LLVM libraries to design a compiler About This Book Learn to use the LLVM libraries to emit intermediate representation (IR) from high-level language Build your own optimization pass for better code generation Understand AST generation and use it in a meaningful way Who This Book Is For This book is intended for those who already know some of the concepts of compilers and want to quickly get familiar with the LLVM infrastructure and the rich set of libraries that it provides. What You Will Learn Get an introduction to LLVM modular design and LLVM tools Convert frontend code to LLVM IR Implement advanced LLVM IR paradigms Understand the LLVM IR Optimization Pass Manager infrastructure and write an optimization pass Absorb LLVM IR transformations Understand the steps involved in converting LLVM IR to Selection DAG Implement a custom target using the LLVM infrastructure Get a grasp of C's frontend clang, an AST dump, and static analysis In Detail LLVM is currently the point of interest for many firms, and has a very active open source community. It provides us with a compiler infrastructure that can be used to write a compiler for a language. It provides us with a set of reusable libraries that can be used to optimize code, and a target-independent code generator to generate code for different backends. It also provides us with a lot of other utility tools that can be easily integrated into compiler projects. This book details how you can use the LLVM compiler infrastructure libraries effectively, and will enable you to design your own custom compiler with LLVM in a snap. We start with the basics, where you'll get to know all about LLVM. We then cover how you can use LLVM library calls to emit intermediate representation (IR) of simple and complex high-level language paradigms. Moving on, we show you how to implement optimizations at different levels, write an optimization pass, generate code that is independent of a target, and then map the code generated to a backend. The book also walks you through CLANG, IR to IR transformations, advanced IR block transformations, and target machines. By the end of this book, you'll be able to easily utilize the LLVM libraries in your own projects. Style and approach This book deals with topics sequentially, increasing the difficulty level in a step-by-step approach. Each topic is explained with a detailed example, and screenshots are included to help you understand the examples. Table of Contents Chapter 1. Playing with LLVM Chapter 2. Building LLVM IR Chapter 3. Advanced LLVM IR Chapter 4. Basic IR Transformations Chapter 5. Advanced IR Block Transformations Chapter 6. IR to Selection DAG phase Chapter 7. Generating Code for Target Architecture

立即下载
Machine Learning Essentials: Practical Guide in R Book preview

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained chapters with practical examples. At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter.

立即下载
Appium Essentials

Appium Essentials,appium的具体操作

立即下载
Redis Essentials 2015版本 PDF高清

Redis-Essentials 书名:redis essentials 页数:203 版本:2015版 格式:PDF高清

立即下载
Open Manage Essentials 部署安装

Open Manage Essentials 平台的搭建安装和部署,并且介绍了windows和linux操作系统使用OMSA

立即下载
MQTT Essentials - A Lightweight IoT Protocol

pdf 文件 如涉及侵权内容,您的资源将被移除 * 请勿上传小说、mp3、图片等与技术无关的内容.一旦发现将被删除 * 请勿在未经授权的情况下上传任何涉及著作权侵权的资源,除非该资源完全由您个人创作 * 点击上传资源即表示您确认该资源不违反资源分享的使用条款,并且您拥有该资源的所有版权或者上传资源的授权

立即下载
Python Data Science Essentials -3rd

ython数据科学概要 第3版 版: 第3版 国际标准书号: 978-1789537864 发表于: 2018年10月20日 页数: 472页 作者:Alberto Boschetti, Luca Massaron 使用流行的数据科学工具从您的数据中获得有用的见解 完全扩展和升级,最新版本的Python Data Science Essentials将帮助您使用最常见的Python库在数据科学操作中取得成功。本书提供了对Python核心的最新见解,包括最新版本的Jupyter Notebook,NumPy,pandas和scikit-learn。 本书涵盖了详细的示例和大型混合数据集,可帮助您掌握数据收集,数据调整和分析,可视化和报告活动的基本统计技术。您还将了解高级数据科学主题,如机器学习算法,分布式计算,调优预测模型和自然语言处理。此外,您还将学习深度学习和渐变增强解决方案,如XGBoost,LightGBM和CatBoost。 在本书的最后,您将全面了解主要的机器学习算法,图形分析技术以及所有可视化和部署工具,以便更轻松地向数据科学专家和企业的受众展示您的结果。 你将学到什么 在Windows,Mac和Linux上设置数据科学工具箱 使用scikit-learn库提供的核心机器学习方法 操纵,修复和探索数据以解决数据科学问题 学习先进的探索和操作技术来解决数据操作问题 优化机器学习模型以优化性能 浏览和聚类图表,利用数据中的互连和链接

立即下载
R Graphics Essentials for Great Data Visualization 1st pdf

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.

立即下载
R.Deep.Learning.Essentials.1785280589

Key Features Harness the ability to build algorithms for unsupervised data using deep learning concepts with R Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models Build models relating to neural networks, prediction and deep prediction Book Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning. This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples. After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models. What you will learn Set up the R package H2O to train deep learning models Understand the core concepts behind deep learning models Use Autoencoders to identify anomalous data or outliers Predict or classify data automatically using deep neural networks Build generalizable models using regularization to avoid overfitting the training data About the Author Dr. Joshua F. Wiley is a lecturer at Monash University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his PhD from the University of California, Los Angeles. His research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and his former work at the UCLA Statistical Consulting Group, Joshua has helped a wide array of clients, ranging from experienced researchers to biotechnology companies. He develops or codevelops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software. Table of Contents Chapter 1. Getting Started with Deep Learning Chapter 2. Training a Prediction Model Chapter 3. Preventing Overfitting Chapter 4. Identifying Anomalous Data Chapter 5. Training Deep Prediction Models Chapter 6. Tuning and Optimizing Models

立即下载
OpenStack Essentials (2nd Edition)

数字版,有目录。 Key Features Navigate through the complex jungle of components in OpenStack using practical instructions This book helps administrators, cloud engineers, and even developers to consolidate and control pools of compute, networking, and storage resources Learn to use the centralized dashboard and administration panel to monitor large-scale deployments Book Description OpenStack is a widely popular platform for cloud computing. Applications that are built for this platform are resilient to failure and convenient to scale. This book, an update to our extremely popular OpenStack Essentials (published in May 2015) will help you master not only the essential bits, but will also examine the new features of the latest OpenStack release - Mitaka; showcasing how to put them to work straight away. This book begins with the installation and demonstration of the architecture. This book will tech you the core 8 topics of OpenStack. They are Keystone for Identity Management, Glance for Image management, Neutron for network management, Nova for instance management, Cinder for Block storage, Swift for Object storage, Ceilometer for Telemetry and Heat for Orchestration. Further more you will learn about launching and configuring Docker containers and also about scaling them horizontally. You will also learn about monitoring and Troubleshooting OpenStack. What you will learn Brush up on the latest release, and how it affects the various components Install OpenStack using the Packstack and RDO Manager installation tool Learn to convert a computer node that supports Docker containers Implement Ceph Block Device images with OpenStack Create and allocate virtual networks, routers and IP addresses to OpenStack Tenants. Configuring and Launching a Docker container. About the Author Dan Radez joined the OpenStack community in 2012 in an operator role. His experience is focused on installing, maintaining, and integrating OpenStack clusters. He has been given the opportunity to internationally present OpenStack content to a range of audiences of varying expertise. In January 2015, Dan joined the OPNFV community and has been working to integrate RDO Manager with SDN controllers and the networking features necessary for NFV. Dan's experience includes web application programming, systems release engineering, and virtualization product development. Most of these roles have had an open source community focus to them. In his spare time, Dan enjoys spending time with his wife and three boys, training for and racing triathlons, and tinkering with electronics projects. Table of Contents RDO Installation Identity Management Image Management Network Management Instance Management Block Storage Object Storage Telemetry Orchestration Docker Scaling Horizontally Monitoring Troubleshooting

立即下载
Nginx-Essentials.pdf

2006 was an exciting year. The disappointment that surrounded the dot-com crash had pretty much been superseded by a renewed and more confident growth of Web 2.0 and inspired a search for technologies of a new age. At that time, I was looking for a web server to power my projects that would do many things in a different way. After getting some experience in large-scale online projects, I knew that the popular LAMP stack was suboptimal and sometimes did not solve certain challenges, such as efficient uploads, geo-dependent rate limiting, and so on. After trying and rejecting a number of options, I came to know about Nginx and immediately felt that my search was over. It is small yet powerful, with a clean code base, good extensibility, relevant set of features, and a number of architectural challenges solved. Nginx definitely stood out from the crowd! I immediately got inspired and felt some affinity to this project. I tried participating in the Nginx community, learned, shared my knowledge, and contributed as much as I could. With time, my knowledge of Nginx grew. I started to get consultancy requests and have been capable of addressing quite sophisticated cases. After some time, I realized that some of my knowledge might be worth sharing with everyone. That's how I started a blog at www.nginxguts.com . A blog turned out to be an author-driven medium. A more reader-focused and more thorough medium was in demand, so I set aside some time to assemble my knowledge in the more solid form of a book. That's how the book you're holding in your hands right now came into existence.

立即下载
Essentials Of Computer Organization And Architecture

Essentials Of Computer Organization And Architecture

立即下载
关闭
img

spring mvc+mybatis+mysql+maven+bootstrap 整合实现增删查改简单实例.zip

资源所需积分/C币 当前拥有积分 当前拥有C币
5 0 0
点击完成任务获取下载码
输入下载码
为了良好体验,不建议使用迅雷下载
img

C++ Essentials

会员到期时间: 剩余下载个数: 剩余C币: 剩余积分:0
为了良好体验,不建议使用迅雷下载
VIP下载
您今日下载次数已达上限(为了良好下载体验及使用,每位用户24小时之内最多可下载20个资源)

积分不足!

资源所需积分/C币 当前拥有积分
您可以选择
开通VIP
4000万
程序员的必选
600万
绿色安全资源
现在开通
立省522元
或者
购买C币兑换积分 C币抽奖
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 4 45
为了良好体验,不建议使用迅雷下载
确认下载
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 0 0
为了良好体验,不建议使用迅雷下载
VIP和C币套餐优惠
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 4 45
您的积分不足,将扣除 10 C币
为了良好体验,不建议使用迅雷下载
确认下载
下载
您还未下载过该资源
无法举报自己的资源

兑换成功

你当前的下载分为234开始下载资源
你还不是VIP会员
开通VIP会员权限,免积分下载
立即开通

你下载资源过于频繁,请输入验证码

您因违反CSDN下载频道规则而被锁定帐户,如有疑问,请联络:webmaster@csdn.net!

举报

若举报审核通过,可返还被扣除的积分

  • 举报人:
  • 被举报人:
  • *类型:
    • *投诉人姓名:
    • *投诉人联系方式:
    • *版权证明:
  • *详细原因: