Data Science at Home

What is deep learning?If you have no patience, deep learning is the result of training many layers of non-linear processing units for feature extraction and data transformation e.g. from pixel, to edges, to shapes, to object classification, to scene description, captioning, etc.

At some point, statistical problems need sampling. Sampling consists in generating observations from a specific distribution.

In this show I interview Sebastian Raschka, data scientist and author of Python Machine Learning.In addition to the fun we had offline, there are great elements about machine learning, data science, current and future trends, to keep an ear on. Moreover, it is the conversation of two data scientists who contribute and operate in the field, on a daily basis.

There are statisticians and data scientists... Among statisticians, there are some who just count. Some others who… think differently. In this show we explore the old time dilemma between frequentists and bayesians.Given a statistical problem, who’s going to be right?

In this episode, we tell you how to become data scientist and join an amazing community that is changing the world with data analytics.

Should data scientists follow the old good practices of software engineering? Data scientists make software after all.

It’s time to experiment with Data Science at home. Since we are still dealing with our hosting service, consider the first episode purely experimental, even though the content might be of your interest, no matter what.

Have you ever thought to get a Big Data infrastructure on your desk? That’s right! On your desk.

Have you ever thought to get a Big Data infrastructure on your desk? That’s right! On your desk.

In this episode I meet Dr Eliseo Ferrante, formerly at the University of Leuven, currently researcher at the Université de Technologie de Compiègne, who studies self-organization and evolution.