▍1. CVRP-GA-master
遗传算法解决处理路径问题 挺好的一个算法(vrp problem solved by GA)
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▍2. tensorflow代码
说明： tensorflow代码神经网络人工智能的简单示例代码，包括源码注释(Tenorflow Code A Simple Example Code for Artificial Intelligence of Neural Networks, Including Source Code Annotations)
▍3. 神经网络原理(课本)
这是一本和神经网络相关的书籍，上课老师推荐的基础教材，内容详细并且经典(This is a book related to neural networks. The basic textbook recommended by the class teacher is detailed and classic.)
▍4. 神经网络原理(课本)
说明： 这是一本和神经网络相关的书籍，上课老师推荐的基础教材，内容详细并且经典(This is a book related to neural networks. The basic textbook recommended by the class teacher is detailed and classic.)
▍5. vae
什么是变分自动编码器? 为了理解VAE，我们首先从最简单的网络说起，然后再一步一步添加额外的部分。一个描述神经网络的常见方法是近似一些我们想建模的函数。然而神经网络也可以被看做是携带信息的数据结构。假如我们有一个带有解卷积层的网络，我们设置输入为值全为1的向量，输出为一张图像。然后，我们可以训练这个网络去减小重构图像和原始图像的平均平方误差。那么训练完后，这个图像的信息就被保留在了网络的参数中。(What is a variational automatic encoder? To understand VAE, we start with the simplest network, and then add additional parts step by step. A common way to describe neural networks is to approximate some of the functions we want to model. However, neural networks can also be regarded as data structures carrying information. If we have a network with deconvolution layer, we set the input to a vector with all values of 1 and output to an image. Then, we can train the network to reduce the average square error between the reconstructed image and the original image. After training, the information of the image is retained in the parameters of the network.vae)
▍6. vae
说明： 什么是变分自动编码器? 为了理解VAE，我们首先从最简单的网络说起，然后再一步一步添加额外的部分。一个描述神经网络的常见方法是近似一些我们想建模的函数。然而神经网络也可以被看做是携带信息的数据结构。假如我们有一个带有解卷积层的网络，我们设置输入为值全为1的向量，输出为一张图像。然后，我们可以训练这个网络去减小重构图像和原始图像的平均平方误差。那么训练完后，这个图像的信息就被保留在了网络的参数中。(What is a variational automatic encoder? To understand VAE, we start with the simplest network, and then add additional parts step by step. A common way to describe neural networks is to approximate some of the functions we want to model. However, neural networks can also be regarded as data structures carrying information. If we have a network with deconvolution layer, we set the input to a vector with all values of 1 and output to an image. Then, we can train the network to reduce the average square error between the reconstructed image and the original image. After training, the information of the image is retained in the parameters of the network.vae)
▍7. Genetic Algorithm Basic
用python编写的遗传算法基本流程，包括变异、交叉、选择等步骤(Basic process of genetic algorithm written in python, including mutation, crossover, selection and other steps)
▍8. Genetic Algorithm Basic
说明： 用python编写的遗传算法基本流程，包括变异、交叉、选择等步骤(Basic process of genetic algorithm written in python, including mutation, crossover, selection and other steps)
▍9. som
这是一个关于som聚类的python代码，用来给数据进行聚类或分类。(This is a python code for som clustering, used to cluster or classify data.)
▍10. som
说明： 这是一个关于som聚类的python代码，用来给数据进行聚类或分类。(This is a python code for som clustering, used to cluster or classify data.)
▍11. Copula应用实例及程序
说明： 读取数据、绘制频率直方图、*计算偏度和峰度*、正态性检验*、求经验分布函数值*、核分布估计**、核分布估计**、*求Copula中参数的估计值**、绘制Copula的密度函数和分布函数图******、求Kendall秩相关系数和Spearman秩相关系数*******、模型评价(My English is poor i hope you can understand from the chinese introdunction thinks)
▍12. ILearnMachineLearning.py-master
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.)
▍13. ILearnMachineLearning.py-master
说明： 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.)
▍14. 8.29(cnn_on_cuda)
python实现cnn简单例子，pytorch框架,cuda(Python implements a simple example of cnn, pytorch framework, CUDA)
▍15. 8.29(cnn_on_cuda)
说明： python实现cnn简单例子，pytorch框架,cuda(Python implements a simple example of cnn, pytorch framework, CUDA)
▍16. imdbatten2
使用多层双向LSTM对输入进行编码，并用于IMDB数据集分类，ACC可以达到92。(Using multi-layer bidirectional LSTM to encode input and classify IMDB data sets, ACC can reach 92.)
▍17. imdbatten2
说明： 使用多层双向LSTM对输入进行编码，并用于IMDB数据集分类，ACC可以达到92。(Using multi-layer bidirectional LSTM to encode input and classify IMDB data sets, ACC can reach 92.)
▍18. Proxy
python网络编程设置代理服务器，转发及缓存机制(python proxy server for caching)
▍19. Proxy
说明： python网络编程设置代理服务器，转发及缓存机制(python proxy server for caching)
▍20. learn ptyhon
说明： 使用双向LSTM神经网络预测时间序列，70%的训练集(Bidirectional LSTM neural networks are used to predict time series)
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