你也能看得懂的Python算法书 code

传统神经网络主要用在三维图片，本资料提供一维卷积神经网络，(Traditional neural networks are mainly used in three-dimensional images. This data provides one-dimensional convolution neural networks.)

说明：  keras学习资料Python深度学习keras（中文版）(Keras learning materials)

说明：  ODL python数据处理，用于医学影像重建，包括MLEM、FBP算法等(ODL Python data processing for medical image reconstruction, including MLEM, FBP algorithm, etc)

说明：  用ABAQUS扩展有限元方法模拟水里压裂的例子。(The program developed in Python language is mainly used to simulate the hydraulic fracture on ABAQUS platform. Plastic damage model is adopted for cement sheath.)

说明：  使用随机森林回归数据，给出数据的重要性和预测(Using random forest regression data, give the importance and prediction of the data)

说明：  从数据库获取车辆在一段时间内的所有行驶记录的相关数据，确定所需数据为GPS经纬度坐标和驾驶时长等，QB模型采用MDF的思想,其基本思想为:通过平均直接翻转距离函数定义两条轨迹之间的距离，两条轨迹需要具有相同的经纬度点数，具有相同点数的轨迹最大的优点是对轨迹距离成对计算，且相同轨迹之间具有更高的分辨率，对于轨迹聚类的结果有一定的优化。(Retrieved from the database cars all over a period of time, record the related data, determine the required data for the GPS latitude and longitude coordinates, and the driving time, QB model by adopting the idea of MDF, its basic idea is: flip directly by the average distance function definition of the distance between two trajectories, two tracks will have the same latitude and longitude points, and has the biggest advantages of the same points of trajectory track distance calculation in pairs, and has higher resolution, between the same trajectory for trajectory clustering results have certain optimization.)

说明：  中文语言理解测评基准，包括代表性的数据集、基准(预训练)模型、语料库、排行榜。 我们会选择一系列有一定代表性的任务对应的数据集，做为我们测试基准的数据集。这些数据集会覆盖不同的任务、数据量、任务难度。(Language Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models, corpus and leaderboard)

【实例简介】 会用到pygame库，没有解压的话不能正常运行

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.)

基于python语言搭建的学生信息管理系统，代码清晰简单便于初学者学习和移植。