python爬虫,模拟登陆，获取人名，微博信息等，多线程

xgboot做预测示例的代码及数据集，仅作为参考使用(The code and data set of xgboot as a prediction example are used for reference only)

1、使用卷积神经网络对用户的满意度与上网数据进行关联(1. Using convolutional neural network to correlate user satisfaction with online data)

通过调用OpenCV库，采用计算相关性大小进行视频跟踪。在源码中输入要跟踪的图样，运行文件，程序可以识别跟踪摄像头摄取场景内与图样相关性最高的目标(By calling the OpenCV library, using a case-by-difference method for video tracking. Input source you want to track the pattern, run the file, the program can identify tracking camera scene and drawings highest goal intake)

用GAN实现样本生成，深度学习框架为TensorFlow(to generate samples)

说明：  推荐推荐实现，基于TensorFlow的推荐(based TensorFlow Recommend systerm)

利用numpy库实现激光器领域的时域行波模型。该模型输出激光器信号，频谱等。(The time-domain traveling wave model in laser field is realized by using numpy library. The model outputs laser signals, spectrum and so on.)

背景图片和文字可以自己更换，音乐是循环播放的

代码用于猫-非猫图片的二分类问题（附件内给出h5格式的数据集），基于Pytorch神经网络工具包，采用比较经典的逻辑回归（Logistic Regression）算法。

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.)

python产生简单的动画，波形连续传递(Python produces simple animations)