Tag Archives: Python

Anomaly Detection using Deep Auto-Encoders

One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a … Continue reading

Posted in Machine Learning | Tagged , , , , , | Leave a comment

6 points to compare Python and Scala for Data Science using Apache Spark

Apache Spark is a distributed computation framework that simplifies and speeds-up the data crunching and analytics workflow for data scientists and engineers working over large datasets. It offers an unified interface for prototyping as well as building production quality application which makes it particularly suitable for an agile approach. I personally believe that Spark will inevitably become the de-facto Big Data framework for Machine Learning and Data Science.

Despite of the different opinions about Spark, let’s assume that a data science team wants to start adopting it as main technology. The choice of programming language is often a dilemma. Shall we build our models in Python or in Scala? Shall we run the exploratory analysis using the iPython notebook or iScala? Continue reading

Posted in Agile, Machine Learning, Python, Scala, Spark | Tagged , , | 13 Comments