Deep Learning is a very hot area of Machine Learning Research, with many remarkable recent successes, such as 97.5% accuracy on face recognition, nearly perfect German traffic sign recognition, or even Dogs vs Cats image recognition with 98.9% accuracy. Many winning entries in recent Kaggle Data Science competitions have used Deep Learning.

The term "deep learning" refers to the method of training multi-layered neural networks, and became popular after papers by Geoffrey Hinton and his co-workers which showed a fast way to train such networks.

Yann LeCun, a student of Geoff Hinton, also developed a very effective algorithm for deep learning, called ConvNet, which was successfully used in late 80-s and early 90-s for automatic reading of amounts on bank checks.

See more on ConvNet and factors enabled recent success of Deep Learning in my exclusive interview with Yann LeCun.

In May 2014, Baidu, the Chinese search giant, has hired Andrew Ng, a leading Machine Learning and Deep Learning expert (and co-founder of Coursera) to head their new AI Lab in Silicon Valley, setting up an AI & Deep Learning race with Google (which hired Geoff Hinton) and Facebook (which hired Yann LeCun to head Facebook AI Lab).

Here are some useful and free (!) resources for learning and using Deep Learning:

• DeepLearning.net, dedicated site for Deep Learning
• DeepLearning.net tutorials
• Deep Learning Wikipedia page
• NYU Deep Learning course material by Yann LeCun
• Yann LeCun overview of Deep Learning with Marc'Aurelio Ranzato
• Geoff Hinton Coursera course on Neural Networks
• Deep Learning: Methods and Applications book (134 pages) from the Microsoft Speech Group
• CMU reading list, including student notes
• Deep Learning Google+ page
• Watch: Deep Learning Tutorial by John Kaufhold at Washington, DC Data Science Meetup, 2014
• Where are the Deep Learning Courses?, blog by John Kaufhold, data scientist and managing partner of Deep Learning Analytics.
• How Deep Learning will change our world, summary of Melbourne Data Science presentation by Jeremy Howard.
  

The packages which support Deep Learning include

• Torch7, an extension of the LuaJIT language which includes an object-oriented package for deep learning and computer vision. The main advantage of Torch7 is that LuaJIT is extremely fast and very flexible.
• Theano + Pylearn2, which has the advantage of using Python (widely used), and the disadvantage of using Python (slow for big data).
• cuda-convnet, High-performance C++/CUDA implementation of convolutional neural networks, based on Yann LeCun work.
  

Excellent Introduction to Deep Learning: From Perceptrons to Deep Networks, by Ivan Vasilev at Toptal

Another excellent presentation Introduction to Parallel Iterative Deep Learning on Hadoop Next-Generation YARN Framework, by Adam Gibson and Josh Patterson.