用于文本提取的图像文本转换

说明：  Python的基础资料，适合新手进行学习，附带讲解的PPT资料(Python basic materials, suitable for beginners to learn, with explanation of the PPT materials)

这个储存库是我的作品和与数据科学和机器学习相关的项目的集合。在我的脚本中，我主要使用python及其专用的库：pandas、numpy、scipy、sci kit learn、matplotlib、basemap plotly。我还用了一些d3进行数据可视化。我还尝试从sci kit学习库中定制算法实现(This repository is a collection of my works and projects related to Data Science and Machine Learning. In my scripts I use mainly Python and its dedicated librarys: Pandas, NumPy, SciPy, Sci-Kit Learn, Matplotlib, Basemap Plotly. I had also used some D3 for data visualizations. I also try to make custom implementations of algorithms known from Sci-Kit Learn library.)

说明：  本书将向您介绍各种有监督和无监督的深度学习算法，例如多层感知器，线性回归和其他更高级的深度卷积和递归神经网络。您还将了解图像处理，手写识别，对象识别等更多信息。(This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.)

说明：  Python的入门书籍，很适合新手进行阅读(Python's introductory books)

测试数据集的代码mapreduce编程

【实例简介】国外开发pyqt的经典书籍

python源码，利用遗传算法解决带时间窗的车辆调度问题。(Python source code, using GA to solve the vehicle scheduling problem with time windows.)

测试数据集的代码mapreduce编程

说明：  Adaboost体现的是“三个臭皮匠，胜过一个诸葛亮”，它是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)， 然后把这些弱分类器集合起来，构成一个更强的最终分类器（强分类器）(Adaboost embodies "three smelly cobblers are better than one Zhuge Liang". It is an iterative algorithm. Its core idea is to train different classifiers (weak classifiers) for the same training set. Then these weak classifiers are combined to form a stronger final classifier (strong classifier).)

利用Python对各类数据分析算法进行编写，文档包括数据预处理方法，相关的分类和回归分析的算法理论及实践，以及一些实例应用分析。(Use Python to write various data analysis algorithms, including data preprocessing methods, algorithm theory and practice of related classification and regression analysis, and some application analysis.)