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Weekly Selection – Nov 10, 2017

Applied Deep Learning – Part 4: Convolutional Neural Networks


By Arden Dertat – 23 min read

Convolutional Neural Networks (CNN) are everywhere. It is arguably the most popular deep learning architecture. The recent surge of interest in deep learning is due to the immense popularity and effectiveness of convnets.


How to get clinical AI tech approved by regulators

By Hugh Harvey – 12 min read

AI in medicine is coming, and there is little to stop it… apart from one pesky hurdle. Whether you are a small three man start-up or a multi-billion dollar international conglomerate, you have to pass the litmus test of medical device regulation.


Predict Employee Turnover With Python

By Susan Li – 9 min read

This post presents a reference implementation of an employee turnover analysis project that is built by using Python’s Scikit-Learn library. In this article, we introduce Logistic Regression, Random Forest, and Support Vector Machine.


5 Must-Have Data Visualizations for B2B Executives

By Payman Taei – 6 min read

The human brain is a powerful tool that can create and process a vast amount of information. If you want to take the shortest route from page or screen to brain, try an image.


Google Colaboratory – Simplifying Data Science Workflow

By Dmitry Rastorguev – 3 min read

Google has recently made public its internal tool for data science and machine learning workflow called Colaboratory. Although it is very similar to Jupyter Notebook upon top of which it is built, the real value comes from the free computing power that this service currently offers.


Analyse the migration of scientific researchers

By Hannah Yan Han – 4 min read

Today I looked into the inter- and intra-continental migration of scientific researchers based on ORCID ( Open Researcher and Contributor ID) data. Since not everyone has ORCID, the dataset is best seen as a directional sample of all researchers, and tracks their earliest/latest countries with research activities as well as their PhD countries.


10 Years of Data Science Visualizations

By Ben Weber – 6 min read

My career in data science started a decade ago, when I took my first machine learning course at UC Santa Cruz. Since then, I’ve worked on a variety of teams and used a number of different visualization tools.


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