On Friday at DOAG Big Data Days, I presented one possible application of deep learning: using deep learning for automatic crack detection – with some background theory, a Keras model trained from scratch, and the use of VGG16 pretrained on Imagenet. The amount of input data really was minimal, and the ...

As some of you may be aware, this is the best time of year. Not only is it finals time but its also Brownlow week. During my honours year my thesis was on trying to predict who would win that years Brownlow medal. I have been running the model ever ...

I am pleased to announce that vtreat version 0.6.0 is now available to R users on CRAN. vtreat is an excellent way to prepare data for machine learning, statistical inference, and predictive analytic projects. If you are an R user we strongly suggest you incorporate vtreat into your projects. vtreat handles, ...

By Gabriel Vasconcelos Regression Trees In this post I am going to discuss some features of Regression Trees an Random Forests. Regression Trees are know to be very unstable, in other words, a small change in your data may drastically … Continue reading →

This post is dedicated to model prices of real estate by an area and a number of rooms using generalized linear model (GLM) and generalized additive model (GAM). Previous post shows how data was obtained, while in the other post the linear model is fitted to the data.

t-SNE is a very powerful technique that can be used for visualising (looking for patterns) in multi-dimensional data. Great things have been said about this technique. In this blog post I did a few experiments with t-SNE in R to learn about this technique and its uses. Its power to ...

There are substantial differences between ad-hoc analyses (be they: machine learning research, data science contests, or other demonstrations) and production worthy systems. Roughly: ad-hoc analyses have to be correct only at the moment they are run (and often once they are correct, that is the last time they are run; ...