复杂网络中常用算法的实现

基于SVM的时序序列预测，用python实现，内附测试数据，方便可用。(SVM prediction based on a timing sequence with python to achieve, enclosing the test data to facilitate available.)

这是关于强化学习的python源代码，强化学习是一种经典的机器学习的分支。该代码包里面主要是利用q-learning的方法，实现了一些基本游戏的控制，仅供大家参考

Python 学习爬虫初步知识，包含爬虫的基础架构，URL管理器，迭代，正则表达式的匹配等等，是初学者学习爬虫知识的良好基础结构。供大家学习交流。

验证是否存在cve-2018-2628漏洞(Verify that there is a cve-2018-2628 vulnerability)

说明：  完成一个基于q-learning算法的简单寻径实例(A simple path finding example based on Q-learning algorithm is completed)

说明：  Excel 转为 json格式， python3 代码(Excel converted to json format, code written in python3)

说明：  在许多情况下，利用深度学习算法搭建的神经网络模型都需妥进行某 种形式的优化。 这非常重要，只有经过优化的网络，才能在训练之后达到 不错的解决问题的效果。 优化的最直接目的就是使参数更加准确地更新。 一般神经网络的训练过程大致可以分为两个阶段：第一个阶段先通过 前向传播算法计算得到预测值，并将预测值和真实值做对比，得出两者之 间的差距；在第二个阶段，通过反向传播算法计算损失函数对每一个参数 的梯度，再根据梯度和学习率使用梯度下降算法更新每一个参数。(In many cases, the neural network model built by deep learning algorithm needs to be optimized in some form. This is very important, only after the optimization of the network, in order to achieve good results in solving problems after training. The most direct purpose of optimization is to update parameters more accurately. The training process of general neural network can be roughly divided into two stages: in the first stage, the predicted value is calculated by the forward propagation algorithm, and the difference between the predicted value and the real value is obtained; in the second stage, the loss function is calculated by the back-propagation algorithm for each parameter According to the gradient and learning rate, the gradient descent algorithm is used to update each parameter.)

《PyQt5快速开发与实战》 第二章节 程序(PyQt5 Rapid Development and Practice Chapter 2 Procedure)

说明：  python实现实时视频流传输和显示，从客户端到服务器端(Python implements real-time video streaming transmission and display from client to server)

现在了faster rcnn