python-machine-learning：scikit-learn和TPOT简介

1. 代码主要基于GPML V4.2工具箱实现 2. 提供了两个应用实例（单变量预测和多变量预测） 3. 给出了预测均值和方差的可视化结果

通过matlab对lstm,rnn,qrnn等算法进行了实现，并针对具体数据的预测对这些算法作比较。

压缩包里含有logistic regression逻辑回归的Python源代码，训练数据集和测试训练集，最后也用Python画了结构示意图。只需要有Numpy和Matplotlib两个包即可。

逻辑回归一般只能解决二分问题，但是进行扩展之后可以解决多线性分类问题。这是一个完整的Softmax regression解决多线性分类的源代码，python3编码，可直接运行，有输入数据和预测数据的可视化编程。还训练部分和测试部分的源代码进行了封装，可直接运行。

SPSS 25 回归方法（Regression）IBM官方说明手册，繁体中文版。

Regression Modeling Strategies.pdf

用cnn回归进行的图像配准，比传统图像配准算法更快更准确的得到配准参数

基于java实现的一元线性回归代码，包括三个类

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