python GUI

用python实现图像的LBP特征提取，参数可以自己修改。(Get a LBP image original binary image with python.)

深度学习的相关资料 喜欢的小伙伴可以赶快下载啊 抓紧 我要入会员(In-depth learning related materials like the small partners can quickly download ah grasp my membership)

machine learning 方面有关于Bayesian Regression的资料，语言是python(There are aspects of machine learning Bayesian Regression information about language is python)

说明：  用于模拟大气湍流、哈特曼、变形镜的python程序代码。(Program for simulating atmospheric turbulence, SHACK- Hartmann, deformable mirror codes)

【实例简介】(最新)强化学习导论：Reinforcement learning - An introduction        2020英文版  作者：Richard S. Sutton and Andrew G. Barto       强化学习权威之作

PYTHON 井字棋,#井字棋 import random #是否重来一次 def playAgain(): print("你想重玩一次吗？（是/否）") return input() == "是" #选择什么符号棋子 def inputPlayerLetter(): letter = "" while not (letter =="X" or letter =="O"): print ("你想选x还是o") letter = input().upper() if letter == "X": return ["X","O"] else: return ["O","X"]

使用python线程池多线程爬取文档网中的文档，目前为指定关键词，采取深度优先策略爬取文档链接、按业下载文档，递归下载每一页，内存消耗较大。

我们提供一类称为MobileNets的高效模型，用于移动和嵌入式视觉应用。?MobileNets是基于一个流线型的架构，它使用深度可分离的卷积来构建轻量级的深层神经网络。我们引入两个简单的全局超参数，在延迟度和准确度之间有效地进行平衡。这两个超参数允许模型构建者根据问题的约束条件，为其应用选择合适大小的模型。我们进行了资源和精度权衡的广泛实验，与ImageNet分类上的其他流行的网络模型相比，MobileNets表现出很强的性能。最后，我们展示了MobileNets在广泛的应用场景中的有效性，包括物体检测，细粒度分类，人脸属性和大规模地理定位。(We provide an efficient model called MobileNets for mobile and embedded vision applications. MobileNets is based on a streamlined architecture that USES deep separable convolution to build a lightweight deep neural network. We introduce two simple global hyperparameters to effectively balance the delay and accuracy. These two hyperparameters allow the model builder to select an appropriate size model for its application based on the constraints of the problem. We conducted extensive experiments on resource and precision tradeoffs, and MobileNets showed strong performance compared with other popular network models on the ImageNet classification. Finally, we demonstrate the effectiveness of MobileNets in a wide range of application scenarios, including object detection, fine-grained classification, face attributes, and large-scale geographic localization.)

爬取百度图片下载到本地（python代码）

《PyQt5快速开发与实战》 第一章节程序(PyQt5 Rapid Development and Practice Chapter I Procedure)