LLVM Cookbook中文版

王欢明译 LLVM Cook Book中文版_高清带书签导航。 内容简介 · · · · · · 《LLVM Cookbook中文版》以任务驱动的方式，带领读者编写基于LLVM 的编译器前端、优化器、后端。通过丰富的实例，读者能够从中理解LLVM 的架构，以及如何使用LLVM 来编写自己的编译器。 相比于传统的介绍编译技术的书籍，此书更偏向于实战，因此适合熟悉编译但对LLVM 比较陌生的人员，也适合正在学习编译技术并且在寻找实战机会的人员。

参考书

Packt - Embedded Linux Development Using Yocto Project Cookbook.2nd.2018

Ansible has a large collection of inbuilt modules to manage various cloud resources. The book begins with the concepts needed to safeguard your credentials and explain how you interact with cloud providers to manage resources. Each chapter begins with an introduction and prerequisites to use the right modules to manage a given cloud provider. Learn about Amazon Web Services, Google Cloud, Microsoft Azure, and other providers. Each chapter shows you how to create basic computing resources, which you can then use to deploy an application. Finally, you will be able to deploy a sample application to demonstrate various usage patterns and utilities of resources.

Key Features Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Book Description TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production About the Author Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artif

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

SQL.Cookbook 《SQL Cookbook中文版》中的SQL 是计算机世界的语言，在用关系数据库开发报表时，将数据放入数据库以及从数据库中取出来，都需要SQL 的知识。很多人以一种马马虎虎的态度在使用SQL，根本没有意识到自己掌握着多么强大的武器。《SQL Cookbook中文版》的目的是打开读者的视野，看看SQL 究竟能干什么，以改变这种状况。 在这《SQL Cookbook中文版》中有150 多个小节，这还仅仅是SQL 所能做的事情的一鳞半爪。解决日常编程问题的解决方案的数量仅取决于需要解决的问题的数量，《SQL Cookbook中文版》没有覆盖所有问题，事实上也不可能覆盖；然而从中可以找到许多共同的问题及其解决方案，这些解决方案中用到许多技巧，读者学到这些技巧就可以将它们扩展并应用到《SQL Cookbook中文版》不可能覆盖的其他新问题上。 毫无疑问，《SQL Cookbook中文版》的目标是让读者看到，SQL 能够做多少一般认为是SQL 问题范围之外的事情。在过去的10 年间，SQL 走过了很长的路，许多过去只能用C 和JAVA等过程化语言解决的典型问题现在都可以直接用SQL 解决了，但是很多开发人员并没有意识到这一事实。《SQL Cookbook中文版》就是要帮助大家认识到这一点。

What you will learn from this book Set up the wx main frame by adding your own Frame class to the application Create two stage widgets by using a three step process Set up an event handler, customize, receive and handle events by using custom controls and event propagation Create tooltips with rich content and add tooltips to controls Retrieve information from users using common Dialogs Enhance design and layout, hide and show controls dynamically during runtime by using sizers Create a custom XRC resource handler class to use custom widget classes in XRC Simplify window layout using the SizedControls library Draw basic shapes in a Device Context and draw gradients with ease by using a GraphicsContext Design a custom dialog class Make multi-threaded wxPython applications Capture output from other applications and display it in a wxPython GUI Access platform specific application and resource directories using wxStandardPaths Embed images into python files using wxPython's embedded image tool and classes Add robust exception handling to an application Bundle a wxPython application for distribution making your applications interface translatable

C++ boost application development cookbook 第二版，含pdf, epub, azw3和源代码