FREE ONLINE BOOKS
​
1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville
2. Neural Networks and Deep Learning by Michael Nielsen
3. Deep Learning by Microsoft Research
​4. Deep Learning Tutorial by LISA lab, University of Montreal 

COURSES
​
1. Machine Learning by Andrew Ng in Coursera
2. Neural Networks for Machine Learning by Geoffrey Hinton in Coursera
3. Neural networks class by Hugo Larochelle from Université de Sherbrooke
4. Deep Learning Course by CILVR lab @ NYU
5. CS231n: Convolutional Neural Networks for Visual Recognition On-Going
6. Probabilistic Graphical Model by Daphne Koller in Coursera
​7. Kevin Duh Class for Deep Net Deep Learning and Neural Network

VIDEO AND LECTURES

1. How To Create A Mind By Ray Kurzweil - Is a inspiring talk
2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
3. Recent Developments in Deep Learning By Geoff Hinton
4. The Unreasonable Effectiveness of Deep Learning by Yann LeCun
5. Deep Learning of Representations by Yoshua bengio
6. Principles of Hierarchical Temporal Memory by Jeff Hawkins
7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
8. Making Sense of the World with Deep Learning By Adam Coates
​9. Demystifying Unsupervised Feature Learning By Adam Coates
​10.Visual Perception with Deep Learning by Yann LeCun

PAPERS
​
1. ImageNet Classification with Deep Convolutional Neural Networks
2. Using Very Deep Autoencoders for Content Based Image Retrieval
3. Learning Deep Architectures for AI
4. CMU’s list of papers

TUTORIALS

1. UFLDL Tutorial 1
2. UFLDL Tutorial 2
3. Deep Learning for NLP (without Magic)
​4. A Deep Learning Tutorial: From Perceptrons to Deep Networks

WEBSITES

1. deeplearning.net
2. deeplearning.stanford.edu
​3. deeplearning.cs.toronto.edu

DATASETS

1. MNIST Handwritten digits
2. Google House Numbers from street view
3. CIFAR-10 and CIFAR-100
4. IMAGENET
5. Tiny Images 80 Million tiny images
6. Flickr Data 100 Million Yahoo dataset
​7. Berkeley Segmentation Dataset 500 

FRAMEWORKS

1. Caffe
2. Torch7
3. Theano
​4. cuda-convn
5. Ccv
6. NuPIC
​7. DeepLearning4J

MISCELLANEOUS

1. Google Plus - Deep Learning Community
2. Caffe Webinar
3. 100 Best Github Resources in Github for DL
4. Word2Vec
5. Caffe DockerFile
6. TorontoDeepLEarning convnet
7. Vision data sets
8. Fantastic Torch Tutorial My personal favourite. Also check out gfx.js
9. Torch7 Cheat sheet

OTHER LINK
1. https://ift6266h13.wordpress.com/home/resources/
2. http://www.dmi.usherb.ca/~larocheh/projects_classrbm.html
3. http://www.slideshare.net/hammawan/deep-neural-networks
4. http://www.iro.umontreal.ca/~bengioy/talks/mlss-austin.pdf
5. http://techtalks.tv/talks/lab/59461/
6.https://www.evernote.com/shard/s433/sh/52b77d5f-a2cf-46f5
9b4c68620f1682be/73527274007c5fa123cd6cc0d8bb10df
7. http://cl.naist.jp/~kevinduh/a/deep2014/140116-ResearchSeminar.pdf

taken from:

Perspectives from Leading Practitioners

​David Beyer

Machine intelligence has been the subject of both exuberance and skepticism for decades. The promise of thinking, reasoning machines appeals to the human imagination, and more recently, the corporate budget. Beginning in the 1950s, Marvin Minksy, John McCarthy and other key pioneers in the field set the stage for today’s breakthroughs in theory, as well as practice. Peeking behind the equations and code that animate these peculiar machines, we find ourselves facing questions about the very nature of thought and knowledge. The mathematical and technical virtuosity of achieve‐ ments in this field evoke the qualities that make us human: Every‐ thing from intuition and attention to planning and memory. As progress in the field accelerates, such questions only gain urgency.

Heading into 2016, the world of machine intelligence has been bus‐ tling with seemingly back-to-back developments. Google released its machine learning library, TensorFlow, to the public. Shortly there‐ after, Microsoft followed suit with CNTK, its deep learning frame‐ work. Silicon Valley luminaries recently pledged up to one billion dollars towards the Open AI institute, and Google developed soft‐ ware that bested Europe’s Go champion. These headlines and ach‐ievements, however, only tell a part of the story. For the rest, we should turn to the practitioners themselves. In the interviews that follow, we set out to give readers a view to the ideas and challenges that motivate this progress. We kick off the series with Anima Anandkumar’s discussion of ten‐ sors and their application to machine learning problems in highdimensional space and non-convex optimization. Afterwards, Yoshua Bengio delves into the intersection of Natural Language Pro‐​cessing and deep learning, as well as unsupervised learning and rea‐ soning. Brendan Frey talks about the application of deep learning to genomic medicine, using models that faithfully encode biological theory. Risto Miikkulainen sees biology in another light, relating examples of evolutionary algorithms and their startling creativity. Shifting from the biological to the mechanical, Ben Recht explores notions of robustness through a novel synthesis of machine intelli‐ gence and control theory. In a similar vein, Daniela Rus outlines a brief history of robotics as a prelude to her work on self-driving cars and other autonomous agents. Gurjeet Singh subsequently brings the topology of machine learning to life. Ilya Sutskever recounts the mysteries of unsupervised learning and the promise of attention models. Oriol Vinyals then turns to deep learning vis-a-vis sequence to sequence models and imagines computers that generate their own algorithms. To conclude, Reza Zadeh reflects on the history and evolution of machine learning as a field and the role Apache Spark will play in its future.

​It is important to note the scope of this report can only cover so much ground. With just ten interviews, it far from exhaustive: Indeed, for every such interview, dozens of other theoreticians and practitioners successfully advance the field through their efforts and dedication. This report, its brevity notwithstanding, offers a glimpse into this exciting field through the eyes of its leading minds.