机器学习实战：基于ScikitLearn和TensorFlow

Opencv新手教程.pdf

Opencv新手教程.pdf

Machine Learning for OpenCV

机器学习应用于opencv，该书是英文版，比较详细介绍了openCV与机器学习相结合的知识点，是一本不错的书籍。

Machine Learning for OpenCV

A practical introduction to the world of machine learning and image processing using OpenCV and Python

Machine Learning for OpenCV__

向您介绍统计学习的基本概念，例如分类和回归。一旦涵盖了所有基础知识，您将开始探索各种算法，例如决策树，支持向量机和贝叶斯网络，并学习如何将它们与其他OpenCV功能相结合。随着本书的进展，您的机器学习技能也将如此，直到您准备好接受当今最热门的话题：深度学习。本书面向熟悉OpenCV的Python程序员;本书将为您提供构建自己的机器学习系统所需的工具和理解，并根据实际的实际任务量身定制。

OpenCV实战项目20讲1.0V.pdf

OpenCV实战项目，经典实例，最快上手，适合想要进入机器视觉领域的你。 OpenCV实战项目20讲

Machine Learning for OpenCV_Intelligent image processing with Python(2017).epub

Chapter 1, A Taste of Machine Learning, will gently introduce you to the different subfields of machine learning, and explain how to install OpenCV and other essential tools in the Python Anaconda environment. Chapter 2, Working with Data in OpenCV and Python, will show you what a typical machine learning workflow looks like, and where data comes in to play. I will explain the difference between training and test data, and show you how to load, store, manipulate, and visualize data with OpenCV and Python. Chapter 3, First Steps in Supervised Learning, will introduce you to the topic of supervised learning by reviewing some core concepts, such as classification and regression. You will learn how to implement a simple machine learning algorithm in OpenCV, how to make predictions about the data, and how to evaluate your model. Chapter 4, Representing Data and Engineering Features, will teach you how to get a feel for some common and well-known machine learning datasets and how to extract the interesting stuff from your raw data. Chapter 5, Using Decision Trees to Make a Medical Diagnosis, will show you how to build decision trees in OpenCV, and use them in a variety of classification and regression problems. Chapter 6, Detecting Pedestrians with Support Vector Machines, will explain how to build support vector machines in OpenCV, and how to apply them to detect pedestrians in images. Chapter 7, Implementing a Spam Filter with Bayesian Learning, will introduce you to probability theory, and show you how you can use Bayesian inference to classify emails as spam or not. Chapter 8, Discovering Hidden Structures with Unsupervised Learning, will talk about unsupervised learning algorithms such as k-means clustering and Expectation-Maximization, and show you how they can be used to extract hidden structures in simple, unlabeled datasets. Chapter 9, Using Deep Learning to Classify Handwritten Digits, will introduce you to the exciting field of deep learning. Starting with the perceptron and multi-layer perceptrons, you will learn how to build deep neural networks in order to classify handwritten digits from the extensive MNIST database. Chapter 10, Combining Different Algorithms into an Ensemble, will show you how to effectively combine multiple algorithms into an ensemble in order to overcome the weaknesses of individual learners, resulting in more accurate and reliable predictions. Chapter 11, Selecting the Right Model with Hyper-Parameter Tuning, will introduce you to the concept of model selection, which allows you to compare different machine learning algorithms in order to select the right tool for the task at hand. Chapter 12, Wrapping Up, will conclude the book by giving you some useful tips on how to approach future machine learning problems on your own, and where to find information on more advanced topics.

Machine Learning for OpenCV-Packt Publishing(2017).pdf

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. As a subfield of data science, machine learning enables computers to learn through experience: to make predictions about the future using collected data from the past. And the amount of data to be analyzed is enormous! Current estimates put the daily amount of produced data at 2.5 exabytes (or roughly 1 billion gigabytes). Can you believe it? This would be enough data to fill up 10 million blu-ray discs, or amount to 90 years of HD video. In order to deal with this vast amount of data, companies such as Google, Amazon, Microsoft, and Facebook have been heavily investing in the development of data science platforms that allow us to benefit from machine learning wherever we go—scaling from your mobile phone application all the way to supercomputers connected through the cloud. In other words: this is the time to invest in machine learning. And if it is your wish to become a machine learning practitioner, too—then this book is for you! But fret not: your application does not need to be as large-scale or influential as the above examples in order to benefit from machine learning. Everyone starts small. Thus, the first step of this book is to introduce you to the essential concepts of statistical learning, such as classification and regression, with the help of simple and intuitive examples. If you have already studied machine learning theory in detail, this book will show you how to put your knowledge into practice. Oh, and don't worry if you are completely new to the field of machine learning—all you need is the willingness to learn. Once we have covered all the basic concepts, we will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. Along the way, you will learn how to understand the task by understanding the data and how to build fully functioning machine learning pipelines.

学习OpenCV(中文版)pdf及随书源码

包含中文版的学习OpenCV的pdf，还有随书一起的源代码 11

opencv 4.1中文官方文档v1.1版.pdf

opencv 4.1中文官方文档v1.1版，快来呀

OpenCV pdf

学习OpenCV的好资料,可以直接复制代码，直接应用到项目中！

OpenCV的pdf教程

openCV的教程，可以使用openCV实现人脸识别，车牌识别，人脸对比等功能

学习OpenCV(中文版).pdf

学习OpenCV的经典教材，亦可作为手册，非常值得参考！

OpenCV.pdf

关于opencv基础函数教程文档,解释一些opencv图像处理比较基础的知识和函数使用。

Machine Learning for OpenCV 原版PDF by Beyeler

I'm glad you're here. It's about time we talked about machine learning. Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. As a subfield of data science, machine learning enables computers to learn through experience: to make predictions about the future using collected data from the past. And the amount of data to be analyzed is enormous! Current estimates put the daily amount of produced data at 2.5 exabytes (or roughly 1 billion gigabytes). Can you believe it? This would be enough data to fill up 10 million blu-ray discs, or amount to 90 years of HD video. In order to deal with this vast amount of data, companies such as Google, Amazon, Microsoft, and Facebook have been heavily investing in the development of data science platforms that allow us to benefit from machine learning wherever we go—scaling from your mobile phone application all the way to supercomputers connected through the cloud.

学习opencvPDF扫描版

pdf版 包含所有章节 入门opencv很好的学习资料

OpenCV-Python-Toturial-中文版.pdf

数字图像处理（第三版）冈萨雷斯，北京大学研究生上课专用ppt课件

Mastering OpenCV with Practical Computer Vision.pdf

Opencv 最新英文图书资料，包含很多实际项目开发介绍，比如一些app开发，嵌入式开发

A Practical Introduction to Computer Vision with OpenCV.pdf

A Practical Introduction to Computer Vision with OpenCV.pdf

Learning OpenCV【官方英文原版PDF电子书】

OpenCV出的第一本书，适合初学者上手。

Machine Learning for OpenCV 无水印原版pdf

Machine Learning for OpenCV 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

OpenCV入门教程.pdf

OpenCV中文入门教程，很基础，OpenCV中文入门教程，很基础

opencv3_学习笔记.pdf

上传自己学习opencv时寻找的opencv3的学习笔记，里面的内容很不错，与大家一起分享

openCV快速入门.pdf

摘要：本文旨在帮助读者快速入门OpenCV，而无需阅读冗长的参考手册。掌握了OpenCV的以下基础知识后，有需要的话再查阅相关的参考手册。

opencv 2007清晰版 pdf

opencv初学者很好的一本书，我就是以这本书开始学习opencv语言的。里面有各种各样的实例代码，很清晰很全面，比网上其他的一些清晰些。代码网上对应的有下。如果大家有问题可以相互讨论学习！

OpenCV 3.2.0 官方文档 高清.pdf版

OpenCV 官方只提供网页版文档，本文档是将官方网页文档转换成的PDF版。文档字迹清晰，具有链接、书签功能，方便查阅，可供离线使用。希望对学习OpenCV的朋友有所帮助。官方文档网址：http://docs.opencv.org/。

2017 Machine Learning for OpenCV Intelligent image processing with Python

Machine Learning for OpenCV: Intelligent image processing with Python by Michael Beyeler (https://www.amazon.com/Machine-Learning-OpenCV-Intelligent-processing/dp/1783980281/ref=sr_1_1?s=amazon-devices&ie=UTF8&qid=1517710318&sr=8-1&keywords=opencv+machine+learning&dpID=41CKBKW8y4L&preST=_SX258_BO1,204,203,200_QL70_&dpSrc=srch) The author is a Postdoctoral Fellow in Neuroengineering and Data Science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. Michael is an active contributor to several open-source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a Ph.D. in Computer Science from the University of California, Irvine as well as a M.Sc. in Biomedical Engineering and a B.Sc. in Electrical Engineering from ETH Zurich, Switzerland. When he is not nerding out on brains, he can be found on top of a snowy mountain, in front of a live band, or behind the piano.

OpenCV 中文版PDF文档

提供OpenCV 包括各种各样的函数 ，开运算，闭运算，神经风格转换

完整车牌号识别程序，可以识别车牌和颜色，可以集成到项目中 支持win7+

基于.Net开发车牌号识别程序，通过控制台输出结果，可以通过启动参数传入地址，集成到项目中。 使用介绍：https://blog.csdn.net/billyyi/article/details/138597795

博客中Kmeans以及FCM算法数据（免积分）

博客中Kmeans以及FCM算法的数据，包括IRIS鸢尾花数据集、Wine葡萄酒数据集、Seed小麦种子数据集、glass数据集、WDBD乳腺癌数据集，下载在直接存入项目文件夹即可，如果下载不了，可以私信我，看到后会及时回复。