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深入浅出Android
《深入浅出Android源代码:基于Android6.0和实际开发案例剖》基于Android6.0源代码和实际案例,分析Android系统的框架原理和具体实现。由于Android框架里模块众多,《深入浅出Android源代码:基于Android6.0和实际开发案例剖》只详细分析了框架里的一部分模块,涉及这些模块的原理、架构及代码实现等各个细节,尽量做到让读者知其然,又知其所以然。在Android官方网站,Android分了4层,从高到低分别是应用程序层、应用程序框架层、系统库层和Linux内核层。《深入浅出Android源代码:基于Android6.0和实际开发案例剖》主要涉及应用程序层、应用程序框架层和系统库层。
希望读者通过《深入浅出Android源代码:基于Android6.0和实际开发案例剖》,能对Android有个更全面、更深入的理解。《深入浅出Android源代码:基于Android6.0和实际开发案例剖》面向的读者主要是Framework框架工程师、系统开发工程师、应用开发工程师和ROM开发工程师。
2017-10-17
重构改善既有代码的设计
Martin Fowler和《重构:改善既有代码的设计》(中文版)另几位作者清楚揭示了重构过程,他们为面向对象软件开发所做的贡献,难以衡量。《重构:改善既有代码的设计》(中文版)解释重构的原理(principles)和最佳实践方式(best practices),并指出何时何地你应该开始挖掘你的代码以求改善。《重构:改善既有代码的设计》(中文版)的核心是一份完整的重构名录(catalog of refactoring),其中每一项都介绍一种经过实证的代码变换手法(code transformation)的动机和技术。某些项目如Extract Method和Move Field看起来可能很浅显,但不要掉以轻心,因为理解这类技术正是有条不紊地进行重构的关键。点击进入该书更多详细信息。
2017-10-17
Python练习集100题
Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido van Rossum during 1985- 1990. Like Perl, Python source code is also available under the GNU General Public License (GPL). This tutorial gives enough understanding on Python programming language.
Audience
This tutorial is designed for software programmers who need to learn Python programming language from scratch.
Prerequisites
You should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages is a plus.
2017-10-17
SAS programming in the pharmaceutical industry
This comprehensive resource provides on-the-job training for statistical programmers who use SAS in the pharmaceutical industry This one-stop resource offers a complete review of what entry- to intermediate-level statistical programmers need to know in order to help with the analysis and reporting of clinical trial data in the pharmaceutical industry. SAS Programming in the Pharmaceutical Industry, Second Edition begins with an introduction to the pharmaceutical industry and the work environment of a statistical programmer. Then it gives a chronological explanation of what you need to know to do the job. It includes information on importing and massaging data into analysis data sets, producing clinical trial output, and exporting data. This edition has been updated for SAS 9.4, and it features new graphics as well as all new examples using CDISC SDTM or ADaM model data structures. Whether you're a novice seeking an introduction to SAS programming in the pharmaceutical industry or a junior-level programmer exploring new approaches to problem solving, this real-world reference guide offers a wealth of practical suggestions to help you sharpen your skills. This book is part of the SAS Press program.
2017-09-30
Microsoft SQL Server Blackbook
Welcome to the world of Microsoft SQL Server! Here is finally a client/server database product that can deliver world-class performance at a price that most enterprises can afford not only to purchase, but also to support. SQL Server’s ease of use, coupled with the incredible feature set that accompanies it, delivers enterprise-level client/server computing to everyone. Microsoft SQL Server Black Book will focus on the tasks involved in harnessing Microsoft SQL Server’s capabilities to create a solid production data server. This book focuses on the current release of Microsoft SQL Server while using many techniques that can be applied as far back as version 4.21.
2017-09-25
Microsoft SQL Server 2012 tutorials - writing transact-sql statements
This book expresses the author’s views and opinions. The information contained in this book is provided without any express, statutory, or implied warranties. Neither the authors, Microsoft Corporation, nor its resellers, or distributors will be held liable for any damages caused or alleged to be caused either directly or indirectly by this book.
2017-09-25
新手福利:Apache Spark入门攻略
时下,我们正处在一个“大数据”的时代,每时每刻,都有各种类型的数据被生产。而在此紫 外,数据增幅的速度也在显著增加。从广义上看,这些数据包含交易数据、社交媒体内容(比 如文本、图像和视频)以及传感器数据。那么,为什么要在这些内容上投入如此多精力,其 原因无非就是从海量数据中提取洞见可以对生活和生产实践进行很好的指导。
2017-09-25
Machine Learning an Algorithmic Perspective
Written in an easily accessible style, this book provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python.
2017-09-24
Bayesian Reasoning and Machine Learning
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
2017-09-24
Machine learning for hackers.
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
2017-09-24
Machine Learning in Action-Python
It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades.
"Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers.
Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.
2017-09-24
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