Suggested reading: getting going with deep learning
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:
Deep learning book (free online) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
Deep learning book (free online) by Michael Nielsen
My Neural Network isn’t working! What should I do?
…you’ll need this!
a very readable tutorial on image generation using deep learning (specifically, GANs)
For Multi Task Learning:
For LSTMs (a popular type of recurrent neural network):