现代C ++编程手册-第二版 这是发行的Modern C ++ Programming Cookbook-2nd Edition的代码库。 它包含从头到尾完成本书所必需的所有支持项目文件。 关于这本书 从现代语言功能开始，每个配方都针对特定的问题，并进行了讨论，以解释该解决方案并提供其工作原理的见解。 您将学习有关核心编程语言的主要概念以及在构建从企业级应用程序到低延迟软件的各种软件时面临的常见任务。 您将以配方的形式了解诸如并发，性能，测试等概念。 这些食谱将确保您可以使您的应用程序安全且快速。 说明和导航 所有代码都组织在文件夹中，每章一个。 有些章节为该章节中的每个配方包含一个头文件。 在这种情况下，头文件称为配方_1_01.h，配方_1_02.h等。其他章节包含多个程序，每个配方一个。 在这种情况下，配方中有其自己的文件夹，其名称如Chapter12_01_1，Chapter12_

这是发行的的代码存储库。 它包含从头到尾完成视频课程所需的所有支持项目文件。 关于视频课程 您是否一直在寻找一门可以在scikit-learn和TensorFlow 2.0中教您有效的机器学习的课程？ 还是您一直想获得有效和熟练的工作知识，以解决如何解决无法通过最新的机器学习技术明确编程的问题？ 如果您熟悉pandas和NumPy，本课程将为您提供所有实用机器学习方法的最新，详细的知识，您可以使用它们来解决无法轻松编程的大多数任务； 您还可以使用根据数据学习并做出预测或决策的算法。 该理论将以Jupyter笔记本中的大量实际示例和代码示例演练为基础。 本课程旨在使您高效地构建算法和模型，并根据针对特定任务定义的成功输出或假设，以最高的准确性来执行这些算法和模型。 在本课程结束时，您将能够通过培训，优化模型并将模型部署到生产中来轻松解决一系列基于行业的机器学习问题。 能够有效地执行此操

国外的一本关于MyBatis的优秀教程。一本很好的参考书！

Unity is a powerful game development engine that provides rich functionalities to create 2D and 3D games. Unity 2D Game Development Cookbook is a practical guide to creating games with Unity. The book aims to serve the purpose of exploring problematic concepts in Unity for 2D game development, offering over 50 recipes that are easy to understand and to implement, thanks to the step-by-step explanations and the custom assets provided. The practical recipes provided in the book show clearly and concisely how to do things right in Unity. By the end of this book, you'll be near "experts" when dealing with Unity. You will also understand how to resolve issues and be able to comfortably offer solutions for 2D game development.

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Bioinformatics with R Cookbook. 包括如何用R分析microarray， RNA-seq，GWAS, NGS 数据， 分析蛋白质和核酸序列等等

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Build, manage, and configure high-performing, reliable NoSQL database for your applications with Cassandra Key Features Write programs more efficiently using Cassandra's features with the help of examples Configure Cassandra and fine-tune its parameters depending on your needs Integrate Cassandra database with Apache Spark and build strong data analytics pipeline

It is not so often in life that you get a second chance. I remember that only days after we stopped editing the first edition, I kept asking myself, "Why didn't I...?", or "What the heck was I thinking saying it like that?", and on and on. In fact, the first project I started working on after it was published had nothing to do with any of the methods in the first edition. I made a mental note that if given the chance, it would go into a second edition. When I started with the first edition, my goal was to create something different, maybe even create a work that was a pleasure to read, given the constraints of the topic. After all the feedback I received, I think I hit the mark. However, there is always room for improvement, and if you try and be everything to all people, you become nothing to everybody. I'm reminded of one of my favorite Frederick the great quotes, "He who defends everything, defends nothing". So, I've tried to provide enough of the skills and tools, but not all of them, to get a reader up and running with R and machine learning as quickly and painlessly as possible. I think I've added some interesting new techniques that build on what was in the first edition. There will probably always be the detractors who complain it does not offer enough math or does not do this, that, or the other thing, but my answer to that is they already exist! Why duplicate what was already done, and very well, for that matter? Again, I have sought to provide something different, something that would keep the reader's attention and allow them to succeed in this competitive field. Before I provide a list of the changes/improvements incorporated into the second edition, chapter by chapter, let me explain some universal changes. First of all, I have surrendered in my effort to fight the usage of the assignment operator <- versus just using =. As I shared more and more code with others, I realized I was out on my own using = and not <-. The first thing I did when under con