Make the most of Kotlin by leveraging design patterns and best practices to build scalable and high performing apps Key Features Understand traditional GOF design patterns to apply generic solutions Shift from OOP to FP; covering reactive and concurrent patterns in a step-by-step manner Choose the best microservices architecture and MVC for your development environment Book Description Design patterns enable you as a developer to speed up the development process by providing you with proven development paradigms. Reusing design patterns helps prevent complex issues that can cause major problems, improves your code base, promotes code reuse, and makes an architecture more robust. The mission of this book is to ease the adoption of design patterns in Kotlin and provide good practices for programmers. The book begins by showing you the practical aspects of smarter coding in Kotlin, explaining the basic Kotlin syntax and the impact of design patterns. From there, the book provides an in-depth explanation of the classical design patterns of creational, structural, and behavioral families, before heading into functional programming. It then takes you through reactive and concurrent patterns, teaching you about using streams, threads, and coroutines to write better code along the way By the end of the book, you will be able to efficiently address common problems faced while developing applications and be comfortable working on scalable and maintainable projects of any size What you will learn Get to grips with Kotlin principles, including its strengths and weaknesses Understand classical design patterns in Kotlin Explore functional programming using built-in features of Kotlin Solve real-world problems using reactive and concurrent design patterns Use threads and coroutines to simplify concurrent code flow Understand antipatterns to write clean Kotlin code, avoiding common pitfalls Learn about the design considerations necessary while choosing between architectures Who This Book Is For This book is for developers who would like to master design patterns with Kotlin to build efficient and scalable applications. Basic Java or Kotlin programming knowledge is assumed Table of Contents Getting Started with Kotlin Working with Creational Patterns Understanding Structural Patterns Getting familiar with Behavioral Patterns Pattern implementation using Functional Programming Exploring Streams Staying reactive Introduction: Threads and Coroutines Designed for concurrency Anti-patterns and Idioms Simplifying microservices and MVC

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron English | 13 Mar. 2017 | ASIN: B06XNKV5TS | 581 Pages | AZW3 | 21.66 MB Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

使用PyCharm进行动手应用开发 这是Packt发布的进行的代码库。 使用PyCharm中的实用编码技术加速您的Python应用程序 这本书是关于什么的？ 如果您是初学者或专业Python用户，希望使用最好的Python IDE之一来提高生产率，那么这本书就适合您。 需要具备Python编程语言的基础知识。 本书涵盖以下激动人心的功能： 探索PyCharm功能以及使其在其他Python IDE中脱颖而出的原因 在PyCharm中设置，配置和自定义Python项目 了解PyCharm如何与Django集成以进行Web开发 探索PyCharm在数据库管理和数据可视化方面的功能 在PyCharm中执行代码自动化，GUI测试和版本控制 集成交互式Python工具（如Jupyter Notebooks）以构建虚拟环境 如果您觉得这本书适合您，请立即获取！ 说明和导航 所有代码都组织在文件

Key Features Get up and running with the fundamentals of RTOS and apply them on STM32 Enhance your programming skills to design and build real-world embedded systems Get to grips with advanced techniques for implementing embedded systems Book Description A real-time operating system (RTOS) is used to develop systems that respond to events within strict timelines. Real-time embedded systems have applications in various industries, from automotive and aerospace through to laboratory test equipment and consumer electronics. These systems provide consistent and reliable timing and are designed to run without intervention for years. This microcontrollers book starts by introducing you to the concept of RTOS and compares some other alternative methods for achieving real-time performance. Once you've understood the fundamentals, such as tasks, queues, mutexes, and semaphores, you'll learn what to look for when selecting a microcontroller and development environment. By working through examples that use an STM32F7 Nucleo board, the STM32CubeIDE, and SEGGER debug tools, including SEGGER J-Link, Ozone, and SystemView, you'll gain an understanding of preemptive scheduling policies and task communication. The book will then help you develop highly efficient low-level drivers and analyze their real-time performance and CPU utilization. Finally, you'll cover tips for troubleshooting and be able to take your new-found skills to the next level. By the end of this book, you'll have built on your embedded system skills and will be able to create real-time systems using microcontrollers and FreeRTOS. What you will learn Understand when to use an RTOS for a project Explore RTOS concepts such as tasks, mutexes, semaphores, and queues Discover different microcontroller units (MCUs) and choose the best one for your project Evaluate and select the best IDE and middleware stack for your project Use professional-grade tools for analyzing and debugging your application Get FreeRTOS-based applications up and running on an STM32 board Who this book is for This book is for embedded engineers, students, or anyone interested in learning the complete RTOS feature set with embedded devices. A basic understanding of the C programming language and embedded systems or microcontrollers will be helpful.

Big Data Science & Analytics: A Hands-On Approach By 作者: Arshdeep Bahga – Vijay Madisetti ISBN-10 书号: 0996025537 ISBN-13 书号: 9780996025539 Edition 版本: 1 出版日期: 2016-04-15 pages 页数: (542 ) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, processing frameworks for batch and real-time analytics, serving databases, web and visualization frameworks. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks as used in the proposed design methodology. We chose Python as the primary programming language for this book. Other languages, besides Python, may also be easily used within the Big Data stack described in this book. We describe tools and frameworks for Data Acquisition including Publish-subscribe messaging frameworks such as Apache Kafka and Amazon Kinesis, Source-Sink connectors such as Apache Flume, Database Connectors such as Apache Sqoop, Messaging Queues such as RabbitMQ, ZeroMQ, RestMQ, Amazon SQS and custom REST-based connectors and WebSocket-based connectors. The reader is introduced to Hadoop Distributed File System (HDFS) and HBase non-relational database. The batch analysis chapter provides an in-depth study of frameworks such as Hadoop-MapReduce, Pig, Oozie, Spark and Solr. The real-time analysis chapter focuses on Apache Storm and Spark Streaming frameworks. In the chapter on interactive querying, we describe with the help of examples, the use of frameworks and services such as Spark SQL, Hive, Amazon Redshift and Google BigQuery. The chapter on serving databases and web frameworks provide an introduction to popular relational and non-relational databases (such as MySQL, Amazon DynamoDB, Cassandra, and MongoDB) and the Django Python web framework. Part III focuses advanced topics on big data including analytics algorithms and data visualization tools. The chapter on analytics algorithms introduces the reader to machine learning algorithms for clustering, classification, regression and recommendation systems, with examples using the Spark MLlib and H2O frameworks. The chapter on data visualization describes examples of creating various types of visualizations using frameworks such as Lightning, pygal and Seaborn.

【高清】这个版本只包含第一部分的前九章的内容，相比于第一版，增加了无监督学习等内容。

ZIgbee协议推荐读物，英文版，PDF，少数经典书籍之一

Hands-On Artificial Intelligence with Java for Beginners Hands-On Artificial Intelligence with Java for Beginners Hands-On Artificial Intelligence with Java for BeginnersHands-On Artificial Intelligence with Java for Beginners Hands-On Artificial Intelligence with Java for Beginners

Packt.Hands-On.Microservices.with.Rust.1789342759.epub

动手人工智能实现网络安全 这是Packt发布的《 的代码库。 实施智能AI系统以防止网络攻击并检测威胁和网络异常 这本书是关于什么的？ 如果您希望使用流行的AI工具和技术来设计智能，防威胁的网络安全系统，那么本书非常适合您。 通过本书，您将学习开发可以检测可疑模式和攻击的智能系统，从而保护您的网络和公司资产。 本书涵盖以下激动人心的功能： 使用AI检测电子邮件威胁，例如垃圾邮件和网络钓鱼 分类APT，零时差和多态恶意软件样本 克服威胁检测中的防病毒限制 通过机器学习预测网络入侵并检测异常 通过深度学习验证生物认证程序的强度 如果您觉得这本书适合您，请立即获取！ 说明和导航 所有代码都组织在文件夹中。 例如， 该代码将如下所示： In [ ]: from sklearn.decomposition import PCA pca = PCA(n_components=2)