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Self Driven Data Science — Issue #37
Here’s this weeks lineup of data-driven articles, stories, and resources delivered faithfully to your inbox for you to consume. Enjoy!
JupyterLab is Ready for Users
JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area.
Building a Deep Neural Net In Google Sheets
Check out this very cool project showing that Deep Convolutional Neural Nets aren’t nearly as intimidating as they sound by implementing one in a simple spreadsheet.
The Best Data Scientists Aren’t Being Discovered
What do AI-savvy companies do differently? The author aims to answer this question as he dives into how top tech companies go about recruiting talented data scientists.
Building a Deep Neural Network to play FIFA 18
Artificial Intelligence bots in gaming used to be built by hand-coding a bunch of rules that impart game-intelligence. Now with the use of deep learning techniques, taking on a project like this is more accessible than ever.
The Random Forest Algorithm
This article serves as an in-depth look at one of the most popular machine learning algorithms out there. Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, can produce great results.
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Self Driven Data Science — Issue #37 was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.
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