Hello everyone and welcome back to Not So Random Software!
It’s been a while since I blogged about neural networks and how to use them to rank a set of items based on the users’ preferences. The blog was in 2015 and a lot has happened since then. I think it might be a good time to recap some of the resources that I stumbled upon over this period. Hope you enjoy this random selection of links!A random article or paper
Applying deep learning to Airbnb search
When I started looking at pairwise ranking in this article a few years ago the community was quite young. I was very pleased to see that in 2018 Airbnb published this article presenting their results using a real production dataset. The article does not only show the end result but also walks you through some of the failures they had along the journey. Inspirational!A random video or podcast
Learning from ranks, learning to rank – Jean-Philippe Vert, Google Brain
In this video published by the Alan Turing Institute, Jean-Philippe Vert from Google Brain presents his research on learning to rank. Lots of formulas ahead, but a very strong foundation if you want to understand how to properly formalize this machine learning problem.
A random book
Neural Networks and Deep Learning, Springer
Because of COVID the Springer website made a number of very high-quality books available for free to download. If you didn’t catch the opportunity now is the time to grab this book on Neural Networks and Deep Learning.
A random tool
Ruby-fann gem for simple neural networks in Ruby
RubyFann, or “ruby-fann” is a ruby gem that binds to FANN (Fast Artificial Neural Network) from within a ruby/rails environment. FANN is a free (native) open-source neural network library, which implements multilayer artificial neural networks, supporting both fully-connected and sparsely-connected networks. It is easy to use, versatile, well documented, and fast. RubyFann makes working with neural networks a breeze using ruby, with the added benefit that most of the heavy lifting is done natively.
A random line of code
Ruby-fann is a set of bindings to the native library written in C. The API is super simple to start with, literally 5 lines of code!
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require 'ruby-fann' train = RubyFann::TrainData.new(:inputs=>[[0.3, 0.4, 0.5], [0.1, 0.2, 0.3]], :desired_outputs=>[[0.7], [0.8]]) fann = RubyFann::Standard.new(:num_inputs=>3, :hidden_neurons=>[2, 8, 4, 3, 4], :num_outputs=>1) fann.train_on_data(train, 1000, 10, 0.1) # 1000 max_epochs, 10 errors between reports and 0.1 desired MSE (mean-squared-error) outputs = fann.run([0.3, 0.2, 0.4]) |
A random quote
If you can’t explain it to a six year old, you don’t understand it yourself.Albert Einstein
