Deep Learning resources


1. Stanford – Fei Fei Li, Karpathy – Convolutional Neural Networks for Visual Recognition (CS231n)

2. New York University –  Yan Lecun – Deep Learning

3. Virginia Tech – Deep Learning for Perception

4. Toronto – G. Hilton – Neural Networks for Machine Learning

5. Toronto – Introduction to Neural Networks for Machine Learning

6. Montreal – Bengio, Lecun – Deep learning summer school 2015

7. CUHK – Xiaogang Wang- Introduction to Deep learning 2015

8. Google – Deep learning 2016 using Tensorflow–ud730

9. Toronto – Deep learning in Computer Vision

10. Oxford – Nando de Freitas – Machine Learning



Free online books

1. Deep Learning – Ian Goodfellow, Yoshua Bengio and Aaron Courville

2. Neural Networks and Deep Learning – Michael Nielsen

3. Deep Learning Tutorial – LISA lab, University of Montreal



An Intuitive Explanation of Convolutional Neural Networks

Convolutional Neural Networks (CNNs): An Illustrated Explanation





2. Deep learning school, Bay Area, 24-25/9/2016

Part 1:

Part 2:

1. Deep learning summer school, Montreal 1-7/8/2016


Deepnet frameworks

There are plenty of deepnet frameworks out there. Two most popular among academic are: Caffe and Torch. Both are very fast with a number of pre-trained models. Arguably, Caffe is more popular than Torch. However, Torch is backed up by Google and Facebook.


  • Key players:
  • Installation:
  • Start point:



  • Key players: Facebook, Google, Li Fei Fei (Stanford course)
  • Installation:
  • Start point:
  • Tutorial:


  • Key players: Google
  • Installation:



If you just want to play around with pre-trained model, go for matlab matconvnets toolbox.