Deploy any machine learning model serverless in AWS

September 16, 2018

When a machine learning model goes into production, it is very likely to be idle most of the time. There are a lot of use cases, where a model only needs to run inference when new data is available. If we do have such a use case and we deploy a model on a server, it will eagerly be checking for new data, only to be disappointed for most of its lifetime and meanwhile you pay for the live time of the server. Read more

Generative Adversarial Networks in Pytorch: The distribution of Art

July 16, 2018

Generative adversarial networks seem to be able to generate amazing stuff. I wanted to do a small project with GANs and in the process create something fancy for on the wall. Therefore I tried to train a GAN on a dataset of art paintings. This post I’ll explore if I’ll succeed in getting a full hd new Picasso on the wall. The pictures above give you a glimplse of some of the results from the model. Read more

Clustering data with Dirichlet Mixtures in Edward and Pymc3

June 5, 2018

Last post I’ve described the Affinity Propagation algorithm. The reason why I wrote about this algorithm was because I was interested in clustering data points without specifying k, i.e. the number of clusters present in the data. This post continues with the same fascination, however now we take a generative approach. In other words, we are going to examine which models could have generated the observed data. Through bayesian inference we hope to find the hidden (latent) distributions that most likely generated the data points. Read more

Algorithm Breakdown: Affinity Propagation

May 18, 2018

On a project I worked on at the ANWB (Dutch road side assistence company) we mined driving behavior data. We wanted to know how many persons were likely to drive a certain vehicle on a regular basis. Naturally k-means clustering came to mind. The k-means algorithm finds clusters with the least inertia for a given k. A drawback is that often, k is not known. For the question about the numbers of persons driving a car, this isn’t that big of a problem as we have a good estimate of what k should be. Read more

Transfer learning with Pytorch: Assessing road safety with computer vision

April 12, 2018

For a project at Xomnia, I had the oppertunity to do a cool computer vision assignment. We tried to predict the input of a road safety model. Eurorap is such a model. In short, it works something like this. You take some cars, mount them with cameras and drive around the road you’re interested in. The ‘Google Streetview’ like material you’ve collected is sent to a crowdsourced workforce (at Amazon they are called Mechanical Turks) to manually label the footage. Read more

Computer build me a bridge

January 14, 2018

In earlier posts I’ve analyzed simple structures with a Python fem package called anaStruct. And in this post I’ve used anaStruct to analyze a very non linear roof ponding problem. Modelling a structure in Python may seem cumbersome in relation to some programs that offer a graphical user interface. For simple structures this may well be the case. However now we’ve got a simple way to programmatically model 2D structures, I was wondering if we could let a computer model these structures for us. Read more

Implementing a Support Vector Machine in Scala

November 27, 2017

This post describes the implementation of a linear support vector machine classifier (SVM) in Scala. Scala is a functional programming language that supports functional programming to a far extend. Because I am exploring Scala at the moment and I like the challenge of functional programming, the SVM will be implemented in a functional manner. We are going to test the SVM on two classes from the Iris dataset. Read more

A nonlinear water accumulation analysis in Python

August 23, 2017

Frames One of my first packages in Python is a program for analysing 2D Frames called anaStruct. I wrote this in the summer of 2016 and learned a lot by doing so. When it was ‘finished’ I was really enthusiastic and eager to give it some purpose in the ‘real’ engineering world. My enthusiasm wasn’t for long though. I wrote a fem package that can compute linear force lines. The real world however isn’t so linear. Read more

Programming a neural network from scratch

July 10, 2017

Intro At the moment of writing this post it has been a few months since I’ve lost myself in the concept of machine learning. I have been using packages like TensorFlow, Keras and Scikit-learn to build a high conceptual understanding of the subject. I did understand intuitively what the backpropagation algorithm and the idea of minimizing costs does, but I hadn’t programmed it myself. Tensorflow is regarded as quite a low level machine learning package, but it still abstracts the backpropagation algorithm for you. Read more

Deep learning music classifier part 2. Computer says no!

June 4, 2017

Recap Last post I described what my motivations were to start building a music classifier, or at least attempt to build one. The post also described how I collected a dataset, extracted important features and clustered the data based on their variance. You can read the previous post here. This post describes how I got my feet wet with classifying music. Spotify kind of sorted the data I’ve downloaded by genre. Read more

(c) 2019 Ritchie Vink.