Where to Learn Deep Learning – Courses, Tutorials, Software
Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning.
By Gregory Piatetsky,
@kdnuggets, May 26, 2014.
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 multilayered neural networks, and became popular after papers by Geoffrey Hinton and his coworkers 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 80s and early 90s 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 cofounder 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:
The packages which support Deep Learning include
Related:
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 multilayered neural networks, and became popular after papers by Geoffrey Hinton and his coworkers 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 80s and early 90s 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 cofounder 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 objectoriented 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).
 cudaconvnet, Highperformance C++/CUDA implementation of convolutional neural networks, based on Yann LeCun work.
Related:
 KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab
 KDnuggets Exclusive: Part 2 of the Interview with Yann LeCun
 How Deep Learning Analytics Mimic the Mind
 Deep Learning Wins Dogs vs Cats competition on Kaggle
Top Stories Past 30 Days

