Deep Learning Resources

Great Papers:
http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf
http://deeplearning.net/reading-list/

Tutorials:

TensorFlow:
https://www.tensorflow.org/versions/r0.7/tutorials/index.html

Theano:
http://deeplearning.net/tutorial/

Matlab:
http://deeplearning.stanford.edu/tutorial/

Caffe:
http://caffe.berkeleyvision.org/tutorial/

Torch:
https://github.com/soumith/cvpr2015/blob/master/Deep%20Learning%20with%20Torch.ipynb

Important Names and associated tutorials/talks:

Hinton:
https://www.cs.toronto.edu/~hinton/nntut.html

LeCun:
http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf

Socher:
http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial

Common Datasets:

MNIST - http://yann.lecun.com/exdb/mnist/

CIFAR - https://www.cs.toronto.edu/~kriz/cifar.html

LSUN - http://lsun.cs.princeton.edu/

IMAGENET - http://www.image-net.org/challenges/LSVRC/

Others:http://deeplearning.net/datasets/

Courses:

https://www.udacity.com/course/deep-learning--ud730 - Basic but uses TensorFlow, good to get a basic understanding
https://www.coursera.org/course/neuralnets - Provides great intuition, a little more challenging
https://cs231n.github.io/ - Great for understanding deep learning for images

How to read a research paper

http://www.sciencemag.org/careers/2016/01/how-read-scientific-paper

References for graphs in R

This link provides pretty simple examples but useful. Adding error bars is not completely straightforward, this link gives a good explanation.

How to write a good research paper and give a good research talk

Good reference for research paper and presentation advice.