Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?
In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.
Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
Make informed decisions by identifying the strengths and weaknesses of different tools
Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
Understand the distributed systems research upon which modern databases are built
Peek behind the scenes of major online services, and learn from their architectures

At most technology companies, you'll reach Senior software engineer, the career level for software engineers, in five to eight years. At the career level, your company's career ladder won't require that you work towards the next promotion; being promoted further is an exception rather than expected. This is also when many engineers are first given an opportunity to move into engineering management. Over the past few years, we've seen a flurry of books unlocking the engineering management career path, like Camille Fournier's The Manager's Path, Julie Zhuo's The Making of a Manager, Lara Hogan's Resilient Management, and even my own An Elegant Puzzle. The engineering management career isn't an easy one, but there are maps available to help navigate it. What if you want to advance your career without becoming an engineering manager? The technical leadership path remains relatively undocumented, hard to navigate, and inconsistent across companies. Staff Engineer is your guide to building your career towards a Staff engineering role, receiving the title, and succeeding within the role.

本书以三个“面向”为导向，即面向经典基础理论与方法，面向国际前沿研究，面向工业界实践，深入浅出地介绍操作系统的理论、架构、设计方法与具体实现。本书是首本以ARM64为主体介绍操作系统的教材，将学术前沿与工业实践充分结合，不仅介绍了现有的Linux宏内核操作系统架构，而且介绍了微内核、外核等操作系统架构。
本书共分为三个部分，其中第一部分(操作系统基础)以纸质版的形式出版，第二部分（操作系统进阶）和第三部分（ChCore课程实验）则以电子版的形式在本书网站发布。第一部分共分为11章，内容包括：操作系统概述、硬件结构、操作系统结构、内存管理、进程与线程、操作系统调度、进程间通信、同步原语、文件系统与存储、设备管理和系统虚拟化。
本书包含大量插图、示例和练习，并融入了典型的操作系统相关的技术问题，既可以作为高等院校计算机专业本科生和研究生的操作系统课程教材，也可以作为工业界从事操作系统相关领域研发工作的专业人员的参考书。

This open access book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data. This computational cognitive modeling approach opens up exciting new directions for research in formal semantics, and linguistics more generally, and offers new ways of (re)connecting semantics and the broader field of cognitive science.
The approach of this book is novel in more ways than one. Assuming the mental architecture and procedural modalities of Anderson’s ACT-R framework, it presents fine-grained computational models of human language processing tasks which make detailed quantitative predictions that can be checked against the results of self-paced reading and other psycho-linguistic experiments. All models are presented as computer programs that readers can run on their own computer and on inputs of their choice, thereby learning to design, program and run their own models. But even for readers who won't do all that, the book will show how such detailed, quantitatively predicting modeling of linguistic processes is possible. A methodological breakthrough and a must for anyone concerned about the future of linguistics! (Hans Kamp)
This book constitutes a major step forward in linguistics and psycholinguistics. It constitutes a unique synthesis of several different research traditions: computational models of psycholinguistic processes, and formal models of semantics and discourse processing. The work also introduces a sophisticated python-based software environment for modeling linguistic processes. This book has the potential to revolutionize not only formal models of linguistics, but also models of language processing more generally. (Shravan Vasishth)

《数学女孩4：随机算法》以“随机算法”为主题，从纯粹的数学和计算机程序设计两个角度对随机算法进行了细致的讲解。内容涉及排列组合、概率、期望、线性法则、矩阵、顺序查找算法、二分查找算法、冒泡排序算法和快速排序算法等。整本书一气呵成，非常适合对数学和算法感兴趣的初高中生以及成人阅读。

The practice of enterprise application development has benefited from the emergence of many new enabling technologies. Multi-tiered object-oriented platforms, such as Java and .NET, have become commonplace. These new tools and technologies are capable of building powerful applications, but they are not easily implemented. Common failures in enterprise applications often occur because their developers do not understand the architectural lessons that experienced object developers have learned. Patterns of Enterprise Application Architecture is written in direct response to the stiff challenges that face enterprise application developers. The author, noted object-oriented designer Martin Fowler, noticed that despite changes in technology--from Smalltalk to CORBA to Java to .NET--the same basic design ideas can be adapted and applied to solve common problems. With the help of an expert group of contributors, Martin distills over forty recurring solutions into patterns. The result is an indispensable handbook of solutions that are applicable to any enterprise application platform. This book is actually two books in one. The first section is a short tutorial on developing enterprise applications, which you can read from start to finish to understand the scope of the book's lessons. The next section, the bulk of the book, is a detailed reference to the patterns themselves. Each pattern provides usage and implementation information, as well as detailed code examples in Java or C#. The entire book is also richly illustrated with UML diagrams to further explain the concepts. Armed with this book, you will have the knowledge necessary to make important architectural decisions about building an enterprise application and the proven patterns for use when building them. The topics covered include * Dividing an enterprise application into layers * The major approaches to organizing business logic * An in-depth treatment of mapping between objects and relational databases * Using Model-View-Controller to organize a Web presentation * Handling concurrency for data that spans multiple transactions * Designing distributed object interfaces

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

Unlike any other programming language, Haskell is purely functional with a strong type system and lazy evaluation. It is arguable the most interesting language but also has the reputation of being one of the most challenging to learn. Learning Haskell doesn't have to be difficult, and this book can help!<br /><br /><i>Learn Haskell</i> teaches readers the Haskell language and functional programming concepts while they hack on interesting problems that are challenging but never frustrating. This example-filled tutorial will take users from the basics to tackling the tough topics. There are lots of crystal-clear illustrations, hands-on exercises, and open-ended tasks that encourage readers to explore Haskell on their own.<br /><br />Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.