用python内置的sklearn库实现神经网络的训练和测试

本构方程，用于高温粘塑性模型。的的疯狂了开发法公关部发的风风光光(umat ffgdkfjoiefkdfj)

基于迪杰斯特拉算法的最短路径规划，基于python平台实现(Shortest path planning based on Dijkstra algorithm and implemented on Python platform)

根据matplotlib下animation官网example总结并重写的例子，了解FuncAnimation的用法

说明：  基于迪杰斯特拉算法的最短路径规划，基于python平台实现(Shortest path planning based on Dijkstra algorithm and implemented on Python platform)

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.)

PyQt与panda3d嵌套编程。Panda3d是一种3d游戏引擎，可开发3d动画；PyQt与Qt软件相似，可编写各种界面程序；本例程可实现Panda3d动画与pyqt的嵌套编程。

说明：  FMCW信号处理是通过python脚本实现的，可以总结为： FMCW IF信号预处理 范围-时间地图构造（RTM） 多普勒时间地图构造（DTM） 角度时间地图构造（ATM）(FMCW signal process was implemented by python script, which can be sum up as: FMCW IF signal pre-process Range-Time-Map construction (RTM) Doppler-Time-Map construction (DTM) Angle-Time-Map construction (ATM))

CSO source code for protein complex prediction in large PPI networks

说明：  关于课本的python数据处理，python基础知识pdf(Python data processing)

说明：  用pyhton编写的IDM模型，可以实现交通流仿真，包括跟驰和换道(The IDM model written by Pyhton can realize the traffic flow simulation, including following and lane changing)