线性回归-梯度下降

IDA插件HexRaysCodeXplorer. Hex-Rays Decompiler plugin for easier code navigation. Right-click context menu in the Pseudocode window shows HexRaysCodeXplorer plugin commands(HexRaysCodeXplorer ================== Hex-Rays Decompiler plugin for better code navigation :octocat: __Authors and Contributors__: Aleksandr Matrosov ([@matrosov](https://github.com/matrosov)) Eugene Rodionov ([@rodionov](https://github.com/rodionov)) __HexRaysCodeXplorer__- Hex-Rays Decompiler plugin for easier code navigation. Right-click context menu in the Pseudocode window shows HexRaysCodeXplorer plugin commands: )

本书首先从简单的思路着手，详细介绍了理解神经网络如何工作所必须的基础知识。第一部分介绍基本的思路，包括神经网络底层的数学知识，第2部分是实践，介绍了学习Python编程的流行和轻松的方法，从而逐渐使用该语言构建神经网络，以能够识别人类手写的字母，特别是让其像专家所开发的网络那样地工作。第3部分是扩展，介绍如何将神经网络的性能提升到工业应用的层级，甚至让其在Raspberry Pi上工作。(This book begins with a brief introduction to the basics necessary to understand how neural networks work. The first part introduces the basic ideas, including the basic mathematical knowledge of the neural network, the second part is the practice, introduces the popular and easy way to learn Python programming, so as to gradually use the language to construct neural networks to recognize human handwritten letters, especially to make them as well as the network developed by experts. Do. Part 3 is an extension of how to improve the performance of neural networks to the level of industrial applications, and even let them work on Raspberry Pi.)

中文版附数据及代码文件 量化投资以Python为工具完整版 以python为工具进行量化投资分析 书中包含python基础、统计学基础、金融理论与投资组合、时间序列与配对交易、技术指标与量化投资

说明：  这是对refinenet的轻量级实现，可以进行实时语义分割(Light-Weight RefineNet for Real-Time Semantic Segmentation)

说明：  数据挖掘作为一种现阶段很有用的方式，应用在各个领域。本文比较详细，可以借鉴看看。(Data mining, as a very useful way at this stage, is applied in various fields. We can learn from it.)

使用深度学习的图像分类的 Python 代码。

说明：  图像融合+深度学习，里面的网络结构可以根据自己的设计进行优化，总体来说还是非常不错的。(Image fusion + deep learnng)

基于大规模知识库的条件聚焦神经问答系统的源码，用的torch7框架，适合初学者。。。。

用python画皮卡丘(基于turtle)

简单的手写体字符识别，利用了k近邻和支持向量机算法(Simple handwritten character recognition, using the k nearest neighbor and support vector machine algorithm)