After weeks of training and optimizing a neural net at some point it might be ready for production. Most deep learning projects never reach this point and for the rest it’s time to think about frameworks and technology stack. In…
data
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…
January 12, 2019 Read more
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…
After getting good results with the Random Forest algorithm in the last post, we will take a look at feed forward networks, which are artificial neural networks. Artificial neural networks consist of many artificial neurons, which are based on the…
In the previous post i showed how to use the Support Vector Machine in Spark and apply the PCA to the features. In this post i wills show how to use Decisions Trees on the titanic data and why its better…
This ist the third part of the Kaggle´s Machine Learning by Disaster challenge where i show, how you can use Apache Spark for model based prediction (supervised learning). This post is about support vector machines. The Support Vector Machine (SVM) is…
The naive bayes classification is a probabilistic classifier, which is used to classify a feature vector to a class. It does a probably wrong assumption, that features are statistically independent to each other. Anyway Apache Spark has implemented Naive Bayes and i…
How did the fare, age or gender affected the probability to survive on the Titanic? And how can we use this information to predict the probability to survive for each passenger? That´s the competition, which is offered by Kaggle to get into machine learning…