《代码的未来》是Ruby之父松本行弘的又一力作。作者对云计算、大数据时代下的各种编程语言以及相关技术进行了剖析，并对编程语言的未来发展趋势做出预测，内容涉及Go、VoltDB、node.js、CoffeeScript、Dart、MongoDB、摩尔定律、编程语言、多核、NoSQL等当今备受关注的话题。
《代码的未来》面向各层次程序设计人员和编程爱好者，也可供相关技术人员参考。

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。

本书是深度学习真正意义上的入门书，深入浅出地剖析了深度学习的原理和相关技术。书中使用Python3，尽量不依赖外部库或工具，从基本的数学知识出发，带领读者从零创建一个经典的深度学习网络，使读者在此过程中逐步理解深度学习。书中不仅介绍了深度学习和神经网络的概念、特征等基础知识，对误差反向传播法、卷积神经网络等也有深入讲解，此外还介绍了深度学习相关的实用技巧，自动驾驶、图像生成、强化学习等方面的应用，以及为什么加深层可以提高识别精度等“为什么”的问题。

《程序员代码面试指南：IT名企算法与数据结构题目最优解（第2版）》是一本程序员代码面试"神书”！书中对IT名企代码面试各类题目的最优解进行了总结，并提供了相关代码实现。针对当前程序员面试缺乏权威题目汇总这一痛点，本书选取将近300道真实出现过的经典代码面试题，帮助广大程序员的面试准备做到接近万无一失。"刷”完本书后，你就是"题王”！《程序员代码面试指南：IT名企算法与数据结构题目最优解（第2版）》采用题目解答的方式组织内容，并把面试题类型相近或者解法相近的题目尽量放在一起，读者在学习本书时很容易看出面试题解法之间的联系，使知识的学习避免碎片化。书中将所有的面试题从难到易依次分为"将”“校”“尉”“士”四个档次，方便读者有针对性地选择"刷”题。本书所收录的所有面试题都给出了最优解讲解和代码实现，并且提供了一些普通解法和最优解法的运行时间对比，让读者真切地感受到最优解的魅力！《程序员代码面试指南：IT名企算法与数据结构题目最优解（第2版）》中的题目全面且经典，更重要的是，书中收录了大量新题和最优解分析，这些内容源自笔者多年来"死磕自己”的深入思考。程序员们做好准备在IT名企的面试中脱颖而出、一举成名了吗？这本书就是你应该拥有的"神兵利器”。当然，对需要提升算法和数据结构等方面能力的程序员而言，《程序员代码面试指南：IT名企算法与数据结构题目最优解（第2版）》的价值也是显而易见的。

HBase是Apache旗下一个高可靠性、高性能、面向列、可伸缩的分布式存储系统。利用HBase技术可在廉价PC 服务器上搭建起大规模的存储化集群。使用HBase可以对数十亿级别的大数据进行实时性的高性能读写，在满足高性能的同时还保证了数据存取的原子性。
本书共分为10章，由浅入深的讲解HBase概念、安装、配置、部署。让读者对HBase先有一个感性认识，再从应用角度，介绍了高级用法、监控和性能调优。既兼顾了初学者也适用于想要深入学习HBase的读者。
本书适合于以前没有接触过HBase，或者了解HBase并希望能够深入掌握的读者，适合HBase应用开发人员和系统管理人员学习使用。

本书基于PostgreSQL 10编写，重点介绍PostgreSQL的丰富特性以及生产实践运维技巧。全书共18章，分为基础篇、核心篇、进阶篇三部分。基础篇包括第1到4章，主要介绍PostgreSQL基础知识，例如安装、客户端工具、数据类型、SQL高级特性等，为读者阅读核心篇和进阶篇做好准备；核心篇包括第5到9章，主要介绍PostgreSQL核心内容，例如体系结构、并行查询、事务与并发控制、分区表、NoSQL特性等；进阶篇包括第10到18章，主要介绍PostgreSQL进阶内容，例如性能优化、物理复制、逻辑复制、备份与恢复、高可用、版本升级、Oracle数据库迁移PostgreSQL实战、PostGIS等。

Operating system performance analysis and tuning leads to a better end-user experience and lower costs, especially for cloud computing environments that pay by the OS instance. Systems Performance, 2nd Edition covers concepts, strategy, tools and tuning for Unix operating systems, with a focus on those that are Linux- and Solaris-based.

本书从实践出发讲解Go语言编程的进阶知识。本书共6章，第1章简单回顾Go语言的发展历史；第2章和第3章系统介绍CGO编程和Go汇编语言的用法；第4章对RPC和Protobuf技术进行深入介绍，并讲述如何打造一个自己的RPC系统；第5章介绍工业级环境的Web系统的设计和相关技术；第6章介绍Go语言在分布式领域的一些编程技术。

深度学习在推荐系统领域掀起了一场技术革命，本书从深度学习推荐模型、Embedding技术、推荐系统工程实现、模型评估体系、业界前沿实践等几个方面介绍了这场技术革命中的主流技术要点。
《深度学习推荐系统》既适合推荐系统、计算广告和搜索领域的从业者阅读，也适合人工智能相关专业的本 科生、研究生、博士生阅读，帮助他们建立深度学习推荐系统的技术框架，通过学习前沿案例 加强深度学习理论与推荐系统工程实践的融合能力。

This book will enable you to apply graph thinking to solve complex problems. If you want to learn how to build architectures for extracting value for your domain’s complex problems, then this book is for you.
You’ll learn how to think about your data as a graph, and how to determine if graph technology is right for your application. The book describes techniques for scalable, real-time, and multimodel architectures that solve complex problems, and shows how companies are successfully applying graph thinking in distributed production environments.
Authors Denise Koessler Gosnell and Matthias Broecheler also introduce the Graph Schema Language, a set of terminology and visual illustrations to normalize how graph practitioners communicate conceptual graph models, graph schema, and graph database design.

Cloud Native Spring in Action teaches you effective Spring and Kubernetes cloud development techniques that you can immediately apply to enterprise-grade applications. It takes you step by step from your first idea through to production, showing how cloud native development can add business value at every stage of the software development lifecycle. As you develop an online bookshop, you’ll learn how to build and test a cloud native app with Spring, containerize it with Docker, and deploy it to the public cloud with Kubernetes. Including coverage of security, continuous delivery, and configuration, this hands-on guide is the perfect primer for navigating the increasingly complex cloud landscape.

Apache Spark’s speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples for this framework using PySpark.
In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You’ll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script.
With this book, you will:
Learn how to select Spark transformations for optimized solutions
Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions()
Understand data partitioning for optimized queries
Design machine learning algorithms including Naive Bayes, linear regression, and logistic regression
Build and apply a model using PySpark design patterns
Apply motif finding algorithms to graph data
Analyze graph data by using the GraphFrames API
Apply PySpark algorithms to clinical and genomics data (such as DNA-Seq)

Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide explores the world of real-time data systems through the lens of these popular technologies and explains important stream processing concepts against a backdrop of interesting business problems.
Mitch Seymour, senior data systems engineer at Mailchimp, introduces you to both Kafka Streams and ksqlDB so that you can choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing. In this book, you’ll learn:
Basic and advanced uses of Kafka Streams and ksqlDB
How to transform, enrich, and process event streams
How to build both stateless and stateful stream processing applications
The different notions of time and the role it plays in stream processing
How to to build event-driven microservices on top of continuous event streams
Features, operational characteristics, deployment patterns, and configuration tips for both technologies

Shows you how to leverage the Go programming language's concurrency and rich standard library to write robust network programs. You'll learn how to write secure, network software in idiomatic Go using the language's latest features.
In Network Programming with Go you'll learn how to leverage Go's concurrency and rich standard library to write robust network programs to create and handle network connections. You'll learn how to craft custom HTTP/2 and SSH servers, build secure network connections with HTTPS, HTTP/2, SSH, TLS, encrypted port forwarding, integrate TLS with free Let's Encrypt certificates, QUIC (HTTP/3), structured logging, and metrics. The author also connects network applications with corresponding cloud offerings such as AWS, Azure, and Google Cloud and shows how to programmatically interact with these providers using their software development kits. Difficult concepts are explained with analogies, diagrams, and examples as you learn to solve common networking problems and write secure software. For professional developers and experienced Go programmers.

In Black Hat Go, you'll learn how to write powerful and effective penetration testing tools in Go, a language revered for its speed and scalability. Start off with an introduction to Go fundamentals like data types, control structures, and error handling; then, dive into the deep end of Go’s offensive capabilities as you learn how to:
Write a simple TCP proxy to use concurrency for non-parallel tasks
Create HTTP clients to perform common web interactions
Write a custom DNS server and proxy
Sniff and process packets using the gopacket library
Create extendable tools to expand program functionality
Write your own exploits with the help of insider techniques
Black Hat Go will show you how to build powerful security tools to pen test huge networks, fast.

If you want to build, iterate and scale NLP systems in a business setting and to tailor them for various industry verticals, this is your guide.
Consider the task of building a chatbot or text classification system at your organization. In the beginning, there may be little or no data to work with. At this point, a basic solution that uses rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated—and often data intensive—ML techniques can be used including deep learning. At each step of this journey, there are dozens of alternative approaches you can take. This book helps you navigate this maze of options.

Every website today is vulnerable to attack and a compromised website can ruin a company's reputation. Consider the Heartland Payment System data breach of 2008: the Heartland attackers made off with 130 million credit card numbers using a SQL injection attack to gain access to the web-servers that handled payment data.
Web Security for Developers covers everything a web developer needs to know about web security. Readers will learn who attackers are and what they have at their disposal, how the Internet and websites operate, and various ways websites get attacked. Author Malcolm McDonald explores common attacks like SQL injection and cross-site scripting, as well as common vulnerabilities like information leakage. The first half of the book describes security concepts every developer should know; the second half delves into the individual vulnerabilities themselves, offering real-world examples and code samples.

Microservices architectures offer great benefits: faster change speeds, better scalability and cleaner, evolvable architectures. But, implementing your first Microservices architecture to get those rewards is difficult. How do you quickly educate your team on all the technical details of execution to maximize your chances of success? How do you survive the first year of bringing your microservices implementation to life? How do you improve your execution?
Making the right implementation decisions is difficult and you don't have the luxury of time to find out if the decisions you are making are the right ones. This book offers a prescriptive guide for building a Microservices architecture to combat that uncertainty. Inside, you will find a step-by-step implementation journey mapped out based on the techniques and architectures that have been proven to work for Microservices systems.
This book solves the following problems for users:
What does a "good" microservices project look like?
Are the decisions you're making for your project the "right" ones?
How do you come up with a good microservices design that fits your own context as quickly as possible?
Where should you spend time thinking/designing and where should you just implement "best practices"?

Natural languages – idioms such as English and Cantonese, Zulu and Amharic, Basque and Nicaraguan Sign Language – allow their speakers to convey meaning and transmit meaning to one another. But what is meaning exactly? What is this thing that words convey and speakers communicate? Few questions are as elusive as this. Yet, few features are as essential to who we are and what we do as human beings as the capacity to convey meaning through language.In this book, Gaetano Fiorin and Denis Delfitto disclose a notion of linguistic meaning that is structured around three distinct, yet interconnected dimensions: a linguistic dimension, relating meaning to the linguistic forms that convey it; a material dimension, relating meaning to the material and social conditions of its environment; and a psychological dimension, relating meaning to the cognitive lives of its users.By paying special attention to the puzzle surrounding first-person reference – the way speakers exploit language to refer to themselves – and by capitalizing on a number of recent findings in the cognitive sciences, Fiorin and Delfitto develop the original hypothesis that meaningful language shares the same underlying logical and metaphysical structure of sense perception, effectively acting as a system of classification and discrimination at the interface between cognitive agents and their ecologies.

Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors—such as drawing, composing music, and completing tasks—by generating an understanding of how its actions affect its environment.
With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You’ll also learn how to apply the techniques to your own datasets.
David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you’ll learn how to make your models learn more efficiently and become more creative.
Get a fundamental overview of deep learning
Learn about libraries such as Keras and TensorFlow
Discover how variational autoencoders work
Get practical examples of generative adversarial networks (GANs)
Understand how autoregressive generative models function
Apply generative models within a reinforcement learning setting to accomplish tasks