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Based on a conversation we had in the Machine Listening Lab last week, here are some blogs and other things you can read when you're - say - a new PhD student who wants to get started with applying/understanding deep learning. We can recommend plenty of textbooks too, but here it's mainly blogs and other informal introductions. Our recommended reading:

• Andrew Ng's coursera course on "Deep Learning" - it's not free to be a student on the course, BUT it is free to "audit" the course, either by signing in, or simply by watching the videos on Youtube
• A brief overview of Deep Learning - a v good intro. (Also: DO read the comments. Some big names give their thoughts.)
• This overview Nature paper is good too
• This blog post series covers the LINEAR ALGEBRA underlying deep learning and numerical optimisation
• Introductions that show all the different types of NN architectures: this one and also this one
• Deep learning book (free online) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
• Deep learning book (free online) by Michael Nielsen

PRACTICAL:

• My Neural Network isn't working! What should I do? ...you'll need this!

ADVANCED:

• a very readable tutorial on image generation using deep learning (specifically, GANs)
• Multi Task Learning
• LSTMs (a popular type of recurrent neural network)