分水岭

说明：  可以利用python opencv实现获取图像上鼠标点击的点的像素的坐标(Python opencv can be used to obtain the coordinates of the pixels clicked by the mouse on the image)

给定10000个用户和他们对10000个电影的评价，然后通过协同过滤或梯度下降算法，用训练集训练数据，预测出用户对未看的电影的评分，并与测试集对比验证预测结果的准确性(You can learn Chinese,and read the Chinese introduction.)

pcv，计算机视觉图像分割，用于图像处理等教程，python(Pcv, computer vision image segmentation, for image processing and other tutorials)

说明：  用python和数据库实现简单的图书馆管理系统(Simple library management system based on Python and database)

与经典的方法相比，使用具有长时间记忆细胞的递归神经网络(RNN)不需要或几乎不需要特征工程。数据可以直接输入到神经网络中，神经网络就像一个黑匣子，可以正确地对问题进行建模。其他研究在活动识别数据集上可以使用大量的特征工程，这是一种与经典数据科学技术相结合的信号处理方法。这里的方法在数据预处理的数量方面非常简单(Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing)

编写了一个简单的python爬虫，使用了第三方库beautifulsoup来解析网页文件，并且实现了cookie登录特定网站访问，这个版本比较简单，后期继续更新

RVE均匀化边界条件，ABAQUS插件，可通过插件对复合材料单胞进行边界条件约束，得到有效弹性常数。(RVE homogenizes boundary conditions)

pyhon写的聊天室 安装： cd /path/to/source python bootstrap.py bin/buildout make sure redis-server is started bin/supervisord [optional] bin/supervisorctl goto localhost:9527(a live chat room built with python Install： cd /path/to/source python bootstrap.py bin/buildout make sure redis-server is started bin/supervisord [optional] bin/supervisorctl goto localhost:9527)

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.)

可以识别手势或者一些简单的图像，可以自己在云端训练模型(You can recognize gestures or simple images, and you can train your own models in the cloud.)