Distilling a Neural Network into a soft decision tree

As part of the commitment to continuous (& cutting edge) research at Razorthink Inc, we are coming up with a series of review papers which will screen through the best of research done in the field of deep learning, machine learning, data science and artificial intelligence in general, across the globe. Each week, we will pick up one research paper, break it down to make it easier to understand, take you through the entire research approach, major takeaways and finally bring in the applicability in real use-cases. Our first pick in the series is "Distilling a Neural Network into a soft decision tree" (download link at the bottom) originally written by Nicholas Frosst & Geoffrey Hinton (Google Brain Team). Deep Neural networks have been proven to be very effective in performing tasks that involve classification and prediction based on the complexity of the data. Most importantly, it is highly useful in situations where the input data has a complex relationship with the target variable and the dimensions of the input data is very high.