For someone who has basic background knowledge in ML and wants to know more about NN and DL, my list would be: * Neural Networks and Deep Learning (http://neur

For someone who has basic background knowledge in ML and wants to know more about NN and DL, my list would be: * Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/) - perfect overview, go over it twice at least (the second time you will understand much of the decisions in the start) * Tensorflow and deep learning, without a PhD (https://www.youtube.com/watch?v=sEciSlAClL8 https://www.youtube.com/watch?v=sEciSlAClL8) - as much as I hate video lectures, this one was worth it; a good complement to the book above * Theano Tutorial (http://deeplearning.net/software/theano/tutorial/index.html http://deeplearning.net/software/theano/tutorial/index.html) - using Theano or TensorFlow takes some getting used to. I found TensorFlow documentation absolutely horrible for beginners, probably because the authors expect users to already know such frameworks. Once you learn Theano you won't have trouble with TensorFlow (if that's what you want to use). Then there are more specific papers, but I guess those depend on the problem at hand.