本故事为世界经典童话之一。安徒生童话《白雪皇后》是安徒生作品中经典的作品之一，又译冰雪女王、白雪女王、冰雪皇后、雪之女王、雪后等，曾被多个国家翻拍成不同版本的电影以及动画。童话中讲述魔鬼的一面镜子的两片碎片注入小男孩加伊（又译凯、凯伊）的内心和眼睛，从而使他变得冷酷无情，并被白雪皇后带入她的冰宫，小女孩格尔达（又译盖尔达）是加伊的好朋友，格尔达亲自寻找白雪皇后并解除加伊身上的诅咒，路上遇到重重困难终被克服。全书为日文版。难词标注假名，供日语学习者学习使用。

本故事为日本经典童话之一。《桃太郎》是日本著名民间故事，讲述从桃子里诞生的桃太郎，用糯米团子收容了小白狗、小猴子和雉鸡后，一起前往鬼岛为民除害的故事。全书为日文版。难词标注假名，供日语学习者学习使用。

本故事为蒲松龄创作的聊斋志异作品之一。描述一个奔波在野林荒坟音的妇人，被逼得现了狐狸的原形，钻进在破庙中躲风雨的穷书生王生的袍子下面，躲过了灭顶之灾。数十年后，王生官拜侍御，生一子，名元丰，从小痴傻。忽一日，一村妇，愿将女儿小翠许给元丰为妻。王生夫妇大喜，盛仪迎娶小翠。小翠美若天仙，可给王生家却带来不少的麻烦。全书为日文版。难词标注假名，供日语学习者学习使用。

《白光》是现代文学家鲁迅于1922年创作的短篇小说，作者通过讲述在科举考试中一个屡屡落第的文人陈士成，听信祖宗传言，受白光的启示在院子里挖银子未果，精神迷幻，到大山里寻宝却坠湖而死的故事，批判了封建社会病态的科考制度，揭示了十年苦读反而无用的读书人的悲惨命运。全书为日文版。难词标注假名，供日语学习者学习使用。

本故事为鲁迅创作的作品之一。村里撑船的七斤，革命时进城，被人剪去了辫子。这天他在城里的咸亨酒店听说皇帝坐了龙庭，要辫子，这消息让他烦心，更让他妻子惶恐。跟七斤有过节的赵七爷特意来恐吓七斤一家，说没了辫子就是要杀头。十多天后，七斤夫妇看出了破绽，于是七斤渐渐在村里恢复了被尊重的地位，一家的生活也回到常态。全书为日文版。难词标注假名，供日语学习者学习使用。

本故事为鲁迅创作的作品之一。这篇小说取材于女娲造人和补天的神话。小说开篇写女娲从梦中惊醒，烦闷、懊恼，自然界则是一片春回大地的绚烂景象。和风吹拂，女娲百无聊赖，心神不安，青春焕发，精力饱满而无处发泄。她走向海边，无意识捏弄软泥，于是创造了人。女娲因生命力受压抑而发挥了劳动创造精神，创造了人类，补好了崩裂的天。这是作者对封建思想意识礼教道德的勇敢挑战和彻底否定，体现了强烈的反封建精神。全书为日文版。难词标注假名，供日语学习者学习使用。

本书以国际高速铁路高标准客运服务为依据，结合我国高速铁路运营实际编写，以旅客出行的全过程体验为主线，涵盖旅行咨询、购票、进站乘车，以及旅客在旅行中所需的部分延伸服务等内容。采用对话式结构，内容针对性强，便于学习掌握，有较强的实用性。本书可供铁路客运职工和相关管理人员工作学习使用，也可供相关院校师生教学参考之用。

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features * Your guide to learning efficient machine learning processes from scratch * Explore expert techniques and hacks for a variety of machine learning concepts * Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn * Get a quick rundown of model selection, statistical modeling, and cross-validation * Choose the best machine learning algorithm to solve your problem * Explore kernel learning, neural networks, and time-series analysis * Train deep learning models and optimize them for maximum performance * Briefly cover Bayesian techniques and sentiment analysis in your NLP solution * Implement probabilistic graphical models and causal inferences * Measure and optimize the performance of your machine learning models Who this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Key Features * Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras * Implement advanced concepts and popular machine learning algorithms in real-world projects * Build analytics, computer vision, and neural network projects Book Description Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects. What you will learn * Understand the Python data science stack and commonly used algorithms * Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window * Understand NLP concepts by creating a custom news feed * Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked * Gain the skills to build a chatbot from scratch using PySpark * Develop a market-prediction app using stock data * Delve into advanced concepts such as computer vision, neural networks, and deep learning Who this book is for This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.

Big data processing and analytics at speed and scale using command line tools. Key Features * Perform string processing, numerical computations, and more using CLI tools * Understand the essential components of data science development workflow * Automate data pipeline scripts and visualization with the command line Book Description The Command Line has been in existence on UNIX-based OSes in the form of Bash shell for over 3 decades. However, very little is known to developers as to how command-line tools can be OSEMN (pronounced as awesome and standing for Obtaining, Scrubbing, Exploring, Modeling, and iNterpreting data) for carrying out simple-to-advanced data science tasks at speed. This book will start with the requisite concepts and installation steps for carrying out data science tasks using the command line. You will learn to create a data pipeline to solve the problem of working with small-to medium-sized files on a single machine. You will understand the power of the command line, learn how to edit files using a text-based and an. You will not only learn how to automate jobs and scripts, but also learn how to visualize data using the command line. By the end of this book, you will learn how to speed up the process and perform automated tasks using command-line tools. What you will learn * Understand how to set up the command line for data science * Use AWK programming language commands to search quickly in large datasets. * Work with files and APIs using the command line * Share and collect data with CLI tools * Perform visualization with commands and functions * Uncover machine-level programming practices with a modern approach to data science Who this book is for This book is for data scientists and data analysts with little to no knowledge of the command line but has an understanding of data science. Perform everyday data science tasks using the power of command line tools.

Power up your network applications with Python programming Key Features * Master Python skills to develop powerful network applications * Grasp the fundamentals and functionalities of SDN * Design multi-threaded, event-driven architectures for echo and chat servers Book Description This Learning Path highlights major aspects of Python network programming such as writing simple networking clients, creating and deploying SDN and NFV systems, and extending your network with Mininet. You’ll also learn how to automate legacy and the latest network devices. As you progress through the chapters, you’ll use Python for DevOps and open source tools to test, secure, and analyze your network. Toward the end, you'll develop client-side applications, such as web API clients, email clients, SSH, and FTP, using socket programming. By the end of this Learning Path, you will have learned how to analyze a network's security vulnerabilities using advanced network packet capture and analysis techniques. This Learning Path includes content from the following Packt products: * Practical Network Automation by Abhishek Ratan * Mastering Python Networking by Eric Chou * Python Network Programming Cookbook, Second Edition by Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker What you will learn * Create socket-based networks with asynchronous models * Develop client apps for web APIs, including S3 Amazon and Twitter * Talk to email and remote network servers with different protocols * Integrate Python with Cisco, Juniper, and Arista eAPI for automation * Use Telnet and SSH connections for remote system monitoring * Interact with websites via XML-RPC, SOAP, and REST APIs * Build networks with Ryu, OpenDaylight, Floodlight, ONOS, and POX * Configure virtual networks in different deployment environments Who this book is for If you are a Python developer or a system administrator who wants to start network programming, this Learning Path gets you a step closer to your goal. IT professionals and DevOps engineers who are new to managing network devices or those with minimal experience looking to expand their knowledge and skills in Python will also find this Learning Path useful. Although prior knowledge of networking is not required, some experience in Python programming will be helpful for a better understanding of the concepts in the Learning Path.

Perform advanced dashboard, visualization, and analytical techniques with Tableau Desktop, Tableau Prep, and Tableau Server Key Features * Unique problem-solution approach to aid effective business decision-making * Create interactive dashboards and implement powerful business intelligence solutions * Includes best practices on using Tableau with modern cloud analytics services Book Description Tableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem. This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL. By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features. What you will learn * Understand the basic and advanced skills of Tableau Desktop * Implement best practices of visualization, dashboard, and storytelling * Learn advanced analytics with the use of build in statistics * Deploy the multi-node server on Linux and Windows * Use Tableau with big data sources such as Hadoop, Athena, and Spectrum * Cover Tableau built-in functions for forecasting using R packages * Combine, shape, and clean data for analysis using Tableau Prep * Extend Tableau’s functionalities with REST API and R/Python Who this book is for Tableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. Put each recipe into practice by bringing the latest offerings of Tableau 2019.x to solve real-world analytics and business intelligence challenges. Some understanding of BI concepts and Tableau is required.

Enhance your programming skills by learning the intricacies of object oriented programming in C# 8 Key Features * Understand the four pillars of OOP; encapsulation, inheritance, abstraction and polymorphism * Leverage the latest features of C# 8 including nullable reference types and Async Streams * Explore various design patterns, principles, and best practices in OOP Book Description Object-oriented programming (OOP) is a programming paradigm organized around objects rather than actions, and data rather than logic. With the latest release of C#, you can look forward to new additions that improve object-oriented programming. This book will get you up to speed with OOP in C# in an engaging and interactive way. The book starts off by introducing you to C# language essentials and explaining OOP concepts through simple programs. You will then go on to learn how to use classes, interfacesm and properties to write pure OOP code in your applications. You will broaden your understanding of OOP further as you delve into some of the advanced features of the language, such as using events, delegates, and generics. Next, you will learn the secrets of writing good code by following design patterns and design principles. You'll also understand problem statements with their solutions and learn how to work with databases with the help of ADO.NET. Further on, you'll discover a chapter dedicated to the Git version control system. As you approach the conclusion, you'll be able to work through OOP-specific interview questions and understand how to tackle them. By the end of this book, you will have a good understanding of OOP with C# and be able to take your skills to the next level. What you will learn * Master OOP paradigm fundamentals * Explore various types of exceptions * Utilize C# language constructs efficiently * Solve complex design problems by understanding OOP * Understand how to work with databases using ADO.NET * Understand the power of generics in C# * Get insights into the popular version control system, Git * Learn how to model and design your software Who this book is for This book is designed for people who are new to object-oriented programming. Basic C# skills are assumed, however, prior knowledge of OOP in any other language is not required.

A hands on approach to learning ReasonML from the perspective of a web developer. Key Features * Hands on learning by building a real world app shell that includes client-side routing and more. * Understand Reason’s ecosystem including BuckleScript and various npm workflows. * Learn how Reason differs from TypeScript and Flow, and how to use it to make refactoring less stressful. Book Description ReasonML, also known as Reason, is a new syntax and toolchain for OCaml that was created by Facebook and is meant to be approachable for web developers. Although OCaml has several resources, most of them are from the perspective of systems development. This book, alternatively, explores Reason from the perspective of web development. You'll learn how to use Reason to build safer, simpler React applications and why you would want to do so. Reason supports immutability by default, which works quite well in the context of React. In learning Reason, you will also learn about its ecosystem – BuckleScript, JavaScript interoperability, and various npm workflows. We learn by building a real-world app shell, including a client-side router with page transitions, that we can customize for any Reason project. You'll learn how to leverage OCaml's excellent type system to enforce guarantees about business logic, as well as preventing runtime type errors.You'll also see how the type system can help offload concerns that we once had to keep in our heads. We'll explore using CSS-in-Reason, how to use external JSON in Reason, and how to unit-test critical business logic. By the end of the book, you'll understand why Reason is exploding in popularity and will have a solid foundation on which to continue your journey with Reason. What you will learn * Learn why Reason is exploding in popularity and why it's the future of React * Become familiar with Reason's syntax and semantics * Learn about Reason's ecosystem: BuckleScript and JavaScript interoperability * Learn how to build React applications with Reason * Learn how to use Reason's type system as a tool to provide amazing guarantees * Gain a solid foundation on which to continue your journey Who this book is for The target audience of this book is web developers who are somewhat familiar with ReactJS and who want to learn why ReasonML is the future of ReactJS.

《明天》讲诉了发生在还具有一点儿古风的鲁镇上在特定的“三个晚上两白天”这个时间段的故事，故事围绕着主人公寡妇单四嫂子失去自己的儿子宝儿这个事件展开全篇的事件叙述。《明天》是鲁迅先生创作的一篇小说，这篇小说之所以叫做明天，无论站在何时说这个词，它都代表将来，非现在。无论作者或主人公，都想在摆脱现实，逃往明天。至于原因，原文斑斑可寻。全书为日文版。难词标注假名，供日语学习者学习使用。

《幸福的家庭》主要讲的是在旧社会一知识分子，为赚稿费养家糊口，于是便打算投稿写一篇符合大众口味的文章。以描写知识分子想象中的幸福家庭的模样及生活方式。知识分子虚构了一个接受了民主思想的恩爱夫妻，过着浪漫、幸福的婚姻生活。与虚构小说情节鲜明对比的是：该知识分子生活困顿，过着为三餐温饱、柴米油盐而烦恼的生活。全书为日文版。难词标注假名，供日语学习者学习使用。

一、正文。正文按场景分类，每个大类中又分若干小项，如“人的身体”中包含“全身”“伤病”“医疗”等小项，这样能让学习者在把握出题范围的同时，了解各个类别中的近义词表达。正文中的例句简短、精练。通过对例句的学习，学习者可以掌握单词*核心的用法。全书所有例句均标注注音假名，以便学习者学习。二、课后练习。练习不仅采用了出题形式中的题型，还根据学习需要，增加了“汉字书写”“读音书写”“词语搭配”等题型，从而帮助学习者更全面地掌握词语的应用知识。三、模拟试题。选择出题频率高的词汇作为考查对象，有助于学习者有效检验和评价自己的学习效果和实战能力。

《六只天鹅》是收录于《格林童话》中的一则童话故事，由格林兄弟搜集编撰。讲述了一场善与恶的斗争，作品女主角是个柔弱的女子，但她却战胜了比她强大得多、有权有势的王后和主教，救出了被王后的魔法变成天鹅的6位哥哥。全书为日文版。难词标注假名，供日语学习者学习使用。

灰姑娘是一个童话故事中的角色，原在欧洲民间广为流传，后来才由法国作家夏尔·佩罗和德国的格林兄弟加以采集编写。灰姑娘的故事向我们传达了这么一个人生历练过程——在任何嘲讽、刁难和欺凌之下，都要吃苦耐劳，忍辱负重地活着，即使极度艰难困苦只要心怀对美好未来的无限憧憬，保持善良与积极的心态，终是会获得幸福生活的。全书为日文版。难词标注假名，供日语学习者学习使用。

《美人鱼的故事》选自安徒生童话，是安徒生先生在1837年的作品。讲述了人鱼公主和王子从相见到相爱再到无法在一起的凄美爱情故事。本故事为世界经典童话之一。故事情节为普通读者熟知。全书为日文版。难词标注假名，供日语学习者学习使用。

本故事为日本经典童话之一,起源于中国。老鼠嫁女又称鼠娶亲、鼠纳妇、老鼠娶亲等，这一古老的传统民间传说在中国很流行。故事情节为普通读者熟知。全书为日文版。难词标注假名，供日语学习者学习使用。