Beginning deep learning with 500 lines of Julia by Deniz Yuret.
From the post:
There are a number of deep learning packages out there. However most sacrifice readability for efficiency. This has two disadvantages: (1) It is difficult for a beginner student to understand what the code is doing, which is a shame because sometimes the code can be a lot simpler than the underlying math. (2) Every other day new ideas come out for optimization, regularization, etc. If the package used already has the trick implemented, great. But if not, it is difficult for a researcher to test the new idea using impenetrable code with a steep learning curve. So I started writing KUnet.jl which currently implements backprop with basic units like relu, standard loss functions like softmax, dropout for generalization, L1-L2 regularization, and optimization using SGD, momentum, ADAGRAD, Nesterov’s accelerated gradient etc. in less than 500 lines of Julia code. Its speed is competitive with the fastest GPU packages (here is a benchmark). For installation and usage information, please refer to the GitHub repo. The remainder of this post will present (a slightly cleaned up version of) the code as a beginner’s neural network tutorial (modeled after Honnibal’s excellent parsing example).
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This tutorial “begins” with you coding deep learning. If you need a bit more explanation on deep learning, you could do far worse than consulting Deep Learning: Methods and Applications or Deep Learning in Neural Networks: An Overview.
If you are already at the programming stage of deep learning, enjoy!
For Julia, Julia (homepage), Julia (online manual), juliablogger.com (Julia blog aggregator), should be enough to get you started.
I first saw this in a tweet by Andre Pemmelaar.