基于正态分布阈值的反转策略

使用谷歌或Bing API子域找到指定域的子域(subdomain dork uses google or Bing API to find subdomains of a specified domain )

说明：  朴素贝叶斯是经典的机器学习算法之一，也是为数不多的基于概率论的分类算法。朴素贝叶斯原理简单，也很容易实现，多用于文本分类，比如垃圾邮件过滤。(Naive Bayes is one of the classical machine learning algorithms, and is also one of the few classification algorithms based on probability theory. Naive Bayesian principle is simple and easy to implement. It is mostly used for text categorization, such as spam filtering.)

模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现(Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set)

说明：  chrome插件，使用vpnso 访问google时使用的插件(chrome use vpn if you use gmail so you use this)

说明：  基于python-opencv的图像匹配代码，包含了SIFT、SURF、AKAZE、ORB方法。43-48行修改图像路径，74-81修改匹配方法。(dev: python3.6+opencv_python==3.4.2+opencv-contrib-python==3.4.2)

参数优化的方法有随机网格法，但此方法较为耗时，且结果不够直观，为了使两个参数同时到达最优，得分最高，将其绘制成三维图展示，直观且运行效率高。

说明：  用市面上的摄像头，可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and network layers, so that each layer can learn more features and greatly improve the generalization ability. The network is deeper, the amount of calculation in each layer is reduced, and the ability of feature extraction is strengthened, so as to improve the accuracy of target classification. On the LFW data set, the accuracy of face recognition reaches 98.40%. In this experiment, mtcnn is introduced into the face detection algorithm. Its backbone network is divided into three convolutional neural networks: p-net, R-Net and o-net. Among them, o-net is the most strict in screening candidate face frames. It will output the coordinates of a human face detection frame and five facial feature points (left eye, right eye, nose, left mouth corner, right mouth corner).)

说明：  根据论文写了下pytorch版的模型，替换dataset可用于训练(According to the paper, the model of Python version is written, and the replacement dataset can be used for training)

说明：  用 Python 机器学习，时间序列模型预测， 循环神经网络(Python Machine Learning, Time Series Model Prediction, recurrent Neural Network)

说明：  代码是采用趋势残差异常值得分模型前的数据处理(Data Processing Before Using Trend Residual Difference)