Weekly Selection — May 10, 2019
By Parul Pandey — 9 min read
An open source tool from Google to easily analyze ML models without the need to code.
By Matthew Stewart, PhD Researcher — 20 min read
Want to turn horses into zebras? Make DIY anime characters or celebrities? Generative adversarial networks (GANs) are your new best friend.
By Fábio Neves — 12 min read
The goal of this project is to build a web scraper that will run and perform searches on flight prices with flexible dates (up to 3 days before and after the dates you select first), for a particular destination.
By Daniel Shenfeld — 5 min read
I have worked with 12 startups. They have spanned verticals from fintech and healthcare to ed-tech and biotech, and ranged from pre-seed to post acquisition. My roles have also varied, from deep-in-the-weeds employee #1 to head of data science and strategic advisor.
By Chitta Ranjan — 8 min read
In this post, we will learn how to implement an autoencoder for building a rare-event classifier. We will use a real-world rare event dataset
By Semi Koen — 10 min read
Traditionally, every time you need to modify the output of your notebook cells, you need to change the code and rerun the affected cells. This can be cumbersome, inefficient and error prone and in the case of a non-technical user it may even be impracticable.
By Will Koehrsen — 13 min read
A useful test for determining if your job can be done by a machine with an application to data scientist
By Daniel Foley — 11 min read
Today’s post is based on a project I recently did in work. I was really excited to implement it and to write it up as a blog post as it gave me a chance to do some data engineering and also do something that was quite valuable for my team.
By Dorien Herremans — 8 min read
Machine learning algorithms have transformed the field of vision and NLP. But what about music? These last few years, the field of music information retrieval (MIR) has been experiencing a rapid growth