Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow(3rd Edition)

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Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it's easy to use and lets you write easy-to-compile and efficient machine code. This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You'll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you'll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image comparison using JuliaFeatures Cluster and classify images with KNN/SVM machine learning algorithms Recognize text in an image using the Tesseract library Use OpenCV to recognize specific objects or faces in images and videos Build neural network and classify images with MXNet Who This Book Is For Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.

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使用Python的动手可解释AI（XAI） 这是发行的的代码存储库。 它包含从头到尾完成本书所必需的所有支持项目文件。 平装：454页 书号ISBN-13 ：9781800208131 出版日期：2020年7月31日 链接 关于这本书 有效地将AI见解转化为业务涉众需要仔细的计划，设计和可视化选择。 描述问题，模型以及变量之间的关系及其发现通常是微妙的，令人惊讶的以及技术上复杂的。 带有Python的动手可解释AI（XAI）将使您能够处理特定的动手机器学习Python项目，这些项目的策略性安排可以增强您对AI结果分析的掌握。 分析包括构建模型，使用可视化解释结果以及集成可理解的AI报告工具和不同的应用程序。 您将在Python，TensorFlow 2，Google Cloud的XAI平台，Google Colaboratory和其他框架中构建XAI解决方案，从而打开机器学习模型

用于sklearn和TensorFlow的学习，暂时没有中文版，内容详细，代码可用

This guide is ideal if you're a professional, manager, or student who wants practical knowledge of analyzing data, without having to get a PhD in statistics. It's also good for people who have a PhD in statistics, but may not know how to write programs that apply statistical methods to real data. Discover how to apply the R language to data analysis through active learning and hands-on demonstration. You'll learn how to use R libraries that useful and reliable for data analysis, and how they can save you time and stress. Learn from a PhD-level statistician who develops and leads R courses Start analyzing data with R, rather than absorb academic statistics concepts Run more powerful analyses and make better-looking graphs Spend less time coding, with ggplot2, plyr, reshape2, and lubridate Learn how to make decisions during a data analysis

This document is designed for use at the GTAP shor course held June 2005 in Crete. It can also be used after the course by participants in conjunction with the RunGTAP and GEMPACK software they take home with them from the course. And, with certain limitations mentioned in the next paragraph, the document will also be very useful to others learning to use RunGTAP.

When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!

Hands-On Mobile and Embedded Development with qt5 Hands-On Mobile and Embedded Development with qt5