本资源包含了神经网络及深度学习的大体介绍，以及代码，以及关于rbm网络的简单程序的例子，希望对大家有所帮助。

RBM在深度学习（deep learning）算法中有着非常重要的应用，本文介绍了RBM的基本概念，并介绍了几种有代表性的算法。作者西安交大张春霞，姬楠楠，王冠伟。

RBM的python代码实现，里面可以改隐含层，和输入层的个数，有训练权重

本C++代码是在手写字符的MatLab代码基础上修改完成。

This is a small library that can train Restricted Boltzmann Machines, and also Deep Belief Networks of stacked RBM's. Train RBM's: %train an RBM with binary visible units and 500 binary hidden model= rbmBB(data, 500); %visualize the learned weights visualize(model.W); Do classification: model= rbmFit(data, 500, labels); prediction= rbmPredict(model, testdata); Train a Deep Belief Network with 500,500,2000 architecture for classification: models= dbnFit(data, [500 500 2000], labels); prediction= dbnPredict(models, testdata); see included example code for more I can be contacted on andrej.karpathy@ gmail. NOTE: This was a class project that I worked on for 1 month and then abandoned development for almost 4 years ago. Please do not send me specific questions about issues with the code or questions on how to do something. I only put this code online in hope that it can be useful to others but cannot fully support it. If you would like pointers to more actively maintained implementations, have a look here (https://github.com/rasmusbergpalm/DeepLearnToolbox) or maybe here (https://github.com/lisa-lab/DeepLearningTutorials) Sorry and best of luck! 原文：http://code.google.com/p/matrbm/