Since Data Science was proposed as the sexiest job of the 21th century in 2012, a lot of people from all kind if different fields started to move to data science or related machine learning roles. Solving complex problems with…
There is a lot of confusion about return_state in Keras. What does ist actually return and how can we use it for stacking RNNs or encoder/decoder models. Hopefully this post makes it a bit clearer. Cell state vs Hidden state…
This is the last post in the series about machine learning in practice. This time the post will be about productionizing machine learning models. I want to share my experience from several production machine learning systems and show how it…
In this post I will show how to use styleGANs on larger images to create customizable images of watches. Additionally, I show how to apply styleGAN on custom data.
This post is about how to snapshot your model based on custom validation metrics. First we define the custom metric, as shown here. In this case we use the AUC score: import tensorflow as tf from sklearn.metrics import roc_auc_score def…
In 2017 and 2018 GANs have significantly contributed to the visibility of artificial intelligence. Impressive images generated by GANs have been published and one was even sold for 432 500 $ in an Christies auction. The good thing with GANs…
Smaller companies and researchers do usually not have the money to invest thousands of dollars or euros into specialized deep learning hardware. You might think cloud instances like AWS EC2 or Google compute are the straightforward cheap solution, but if…
Preprocessing and data transformation are the most important parts of all machine learning pipelines. No matter what type of model you use, if the preprocessing pipeline is buggy, your model will deliver wrong predictions . This remains also true, if…
In this How-To series, I want to share my experience with machine learning models in productions environments. This starts with the general differences to typical software projects and how to acquire and deal with data sets in such projects, goes…
Keras offers some basic metrics to validate the test data set like accuracy, binary accuracy or categorical accuracy. However, sometimes other metrics are more feasable to evaluate your model. In this post I will show three different approaches to apply…