Getting Pycharm, Jupyter, and Virtual Environments to play nice

👨🏻‍💻Hacking together a quick shell script to get them to work together

I love Pycharm for many reasons — interactive debugging, linting, autocompletions, integrated Git tools and super-easy environment management are some of them.

One of the things I don’t love about (the free Community Edition of) Pycharm is that it doesn’t come with support for Jupyter Notebooks, which is an indispensable tool for any Data Science project.

So what’s a Data Scientist to do when they want to use Notebooks to do EDA on a dataset but also have access to Pycharm’s full-featured development environment? …


Photo by Felix Mittermeier on Unsplash

This is the 5th article in our MAFAT Radar competition series, where we take an in-depth look at the different aspects of the challenge and our approach to it. If you want a recap, check out previous posts: the introduction, the dataset, augmentations, and visualizing signal data.

When dealing with a Supervised Machine Learning task one of the most important things you need is data — and lots of it. What does one do when faced with a small amount of data, especially for a task that requires Deep Neural Networks? …


Photo by Paweł Czerwiński on Unsplash

This is the 2nd article in our MAFAT Radar competition series, where we take an in-depth look at the different aspects of the challenge and our approach to it. If you want a recap, check out this post.

Let’s jump straight in.

The competition organizers give a clear explanation of the data they provide:

The dataset consists of signals recorded by ground doppler-pulse radars. Each radar “stares” at a fixed, wide area of interest. Whenever an animal or a human moves within the radar’s covered area, it is detected and tracked. The dataset contains records of those tracks. The tracks…


Photo by Clay Banks on Unsplash

Participating in Data Science competitions is one of the main ways Data Scientists can get experience working with “real world” datasets without working professionally in the field. Winning a competition is a huge achievement and can be the ticket to lucrative offers from top AI companies. Starting with competitions is often recommended for entry-level Data Scientists and students to build experience with the craft of Machine Learning.

Kaggle is the most popular and well known of these platforms, and since launching in 2010 it has exploded in popularity alongside the of ML and Data Science boom. …

Adam Cohn

Love working at the intersection of Data, Business & Code. Fascinated by AI, Philosophy, Strategy & History. Fear is the mind-killer

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