Deep Learning Tutorials

http://deeplearning.net/tutorial/

http://deeplearning.net/tutorial/contents.html

Deep Learning Tutorials

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms.

Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. For more about deep learning algorithms, see for example:

• The monograph or review paper Learning Deep Architectures for AI (Foundations & Trends in Machine Learning, 2009).
• The ICML 2009 Workshop on Learning Feature Hierarchies webpage has a list of references.
• The LISA public wiki has a reading list and a bibliography.
• Geoff Hinton has readings from last year’s NIPS tutorial.

The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

The algorithm tutorials have some prerequisites. You should know some python, and be familiar with numpy. Since this tutorial is about using Theano, you should read over the Theano basic tutorial first. Once you’ve done that, read through our Getting Startedchapter – it introduces the notation, and [downloadable] datasets used in the algorithm tutorials, and the way we do optimization by stochastic gradient descent.

The purely supervised learning algorithms are meant to be read in order:

1. Logistic Regression - using Theano for something simple
2. Multilayer perceptron - introduction to layers
3. Deep Convolutional Network - a simplified version of LeNet5

The unsupervised and semi-supervised learning algorithms can be read in any order (the auto-encoders can be read independently of the RBM/DBN thread):

• Auto Encoders, Denoising Autoencoders - description of autoencoders
• Stacked Denoising Auto-Encoders - easy steps into unsupervised pre-training for deep nets
• Restricted Boltzmann Machines - single layer generative RBM model
• Deep Belief Networks - unsupervised generative pre-training of stacked RBMs followed by supervised fine-tuning

Building towards including the mcRBM model, we have a new tutorial on sampling from energy models:

• HMC Sampling - hybrid (aka Hamiltonian) Monte-Carlo sampling with scan()

Building towards including the Contractive auto-encoders tutorial, we have the code for now:

• Contractive auto-encoders code - There is some basic doc in the code.

Energy-based recurrent neural network (RNN-RBM):

• Modeling and generating sequences of polyphonic music

Deep Learning 的Matlab工具箱

2013年05月29日  ⁄ 机器学习, 程序设计  ⁄ 评论数 2 ⁄ 被围观 3,919 views+

DeepLearnToolbox

看起来是一个硕士生的毕业论文内容。不得不感叹大牛的学生水平就是高啊。

这个工具箱已经公开在了 github 网站上：https://github.com/rasmusbergpalm/DeepLearnToolbox

这位就是工具箱的作者。(image credit: Rasmus Berg Palm)

下面是工具箱的介绍，对这方面有兴趣的童鞋可以下载试用，欢迎留言反馈感想！

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A Matlab toolbox for Deep Learning.

Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. A good overview of the theory of Deep Learning theory is Learning Deep Architectures for AI

For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.

• The Next Generation of Neural Networks (Hinton, 2007)
• Recent Developments in Deep Learning (Hinton, 2010)
• Unsupervised Feature Learning and Deep Learning (Ng, 2011)

If you use this toolbox in your research please cite Prediction as a candidate for learning deep hierarchical models of data