python3 TOPK算法求解

基于open MV的人脸检测与识别,闲来无事，兴趣为之(ji yu openMV de re lian shi bie)

This example demonstrates how to read a raw file in Google-App-Engine. 1. Change name of application in app.yml to the name of you application 2. Upload the files 3. Open the main page

说明：  kaggle--入门题，房价预测，常用机器学习算法。(Kaggle entry questions, house price forecasts.)

使用python对rabbitmq的简单练习demo(Simple practice demo of rabbitmq using Python)

使用python语言中的tensorflow框架实现各种机器学习常用算法(machine learning, tensorflow)

用Python的requests方法获取亚马逊网站源代码，通过观察关键词网址模拟浏览器访问，加个代理规避网站反爬虫，使用多进程提高效率，轻松获取源代码

了解基本编程知识，熟悉编程算法，递归算法画五角星。(Understand basic programming knowledge, familiar with programming algorithm, recursive algorithm drawing pentagon.)

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.)

说明：  小甲鱼零基础python视频课程，课后习题(python Homework problems)

说明：  使用卷积神经网络进行高光谱遥感数据分类，使用的数据源为Pavia University高光谱数据 文件夹log--日志文件夹，存放TensorBorad日志、网络参数文件、混淆矩阵图 文件夹Patch--存放数据处理的切片结果 文件夹PaviaU--高光谱数据下载存放位置 文件夹predicted--CNN对原始影像的分类结果 data.py--对原始高光谱影像进行数据处理，生成切片 net.py--神经网络模型 train.py--训练神经网络 utils.py--需要用到的函数 show.py--使用训练好的神经网络模型对原始数据进行分类(Using convolution neural network to classify hyperspectral remote sensing data, the data source is Pavia University hyperspectral data. Folder log -- Log folder, store TensorBorad logs, network parameter files, obfuscation matrix diagrams Folder Patch -- Stores slice results of data processing Folder PaviaU -- Place for Hyperspectral Data Download and Storage Folder predicted -- CNN classification results of original images Data.py -- Data processing of original hyperspectral images to generate slices Net.py--Neural Network Model Train.py--Training Neural Network Utils. py -- Functions to be used Show.py -- Classification of raw data using trained neural network model)