Python绘制椭圆偏振光

说明：  本文针对滑坡预测预报这一复杂性问题提出一种基于前向反馈传播（BP）神经网络的预测模型，描述了滑坡系统输入输出之间的非线性关系。为克服 BP 网络后期收敛速度慢、易陷入局部极小点等缺点，以遗传算法（GA）和模拟退火算法（SA）相结合的方式即遗传-模拟退火（GSA）算法对网络权值进行优化。用该方法预测滑坡位移获得了较好的效果(In this paper, a prediction model based on forward feedback propagation (BP) neural network is proposed for the complex problem of landslide prediction. The nonlinear relationship between input and output of landslide system is described. In order to overcome the shortcomings of BP network, such as slow convergence speed and easy to fall into local minima, genetic algorithm (GA) and simulated annealing algorithm (SA) are combined to optimize the network weight. The method can be used to predict the displacement of landslides)

说明：  它非常适合初学者学习python，一个简单易懂的例子，让初学者可以快速学会猜数的小游戏，便于学习者快速掌握python的语法和结构。(It is very suitable for beginners to learn python, a simple and easy to understand example, so that beginners can quickly learn to guess the number of small games, easy for learners to quickly master Python syntax and structure.)

说明：  利用深度学习进行遥感图像场景分类 这里我们对NWPU-RESISC45数据集的场景图像进行分类 我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning Here we classify scene images from NWPU-RESISC45 dataset We apply convolutional neural network to image classification. We start training data sets from scratch. In addition, a pre-trained VGG16 abd ResNet50 is used for migration learning.)

说明：  该工具是对非线性问题进行求解的，可通过MATLAB，Python等语言进行实现(The tool is used to solve nonlinear problems, which can be realized by matlab,python and other languages.)

说明：  使用协同过滤的推荐系统测试代码Predicting movie ratings using collaborative filtering and latent concept extractors based on matrix decomposition methods like SVD and CUR on the movielens-100k dataset The project explores techniques for predicting unknown user ratings based on a large dataset of users and movie ratings.(Recommender-SystemsWe used 6 different recommender methods to predict user ratings across the data sets. Namely we set up Collaborative Filtering along with its baseline variant and Matrix decomposition techniques like Singular Value Decomposition and CUR. Moreover, we also implemented dimensionality reduction in these matrix decomposition techniques which preserved 90% of the energy so as to increase the computational efficiency while keeping the accuracy similar for these methods.)

说明：  python读取csv 文件，提供了神经网络的程序，以及自编码器(Python reading CSV file)

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说明：  查找图像的局部极大值，功能强大，速度快，精度高(Finding the local maximum of an image is powerful, fast and accurate.)

说明：  人工蜂群算法 算法 用户均衡模型 frankwolfe算法(Artificial Bee Colony Algorithms User Equilibrium Model Frankwolfe Algorithms)

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