Ethan Rosenthal

I’m incredibly excited to finally introduce confingy to the world.

I’m not saying reproducibility is an unworthy aspiration.

As for the ever popular Python vs R vs Julia vs Scala...

TPS Report Like many Python data people, If I need to put together a proper analysis or report, I typically reach for Jupyter notebooks.

I recently wrapped up 4 years at a Big Company.

Spoiler alert: the answer is maybe!

Bio Ethan Rosenthal lives in New York City and works at Runway.

In Need of a Good Editr Growing up, I had always considered myself a decent writer based on my decent grades in English class.

Physics is a macho field.

In my last post, I strongly encouraged monitoring Machine Learning (ML) models with streaming databases.

A very silly blog post came out a couple months ago about The Unbundling of Airflow.

Just like every other scientist, engineer, or Matt, I’m pretty into rock climbing.

I make Python packages for everything.

You Can Not Measure What You Do Not Care To Manage When I started my first data scientist job in 2015, the team I joined had a recommendation system that would run every night to compute new recommendations for all users of our platform.

I had a kid at the start of the year.

I told myself I wouldn’t do it again.

Two years ago, I tried to build a SaaS product for monitoring machine learning models.

Features stores are now becoming a thing.

When you breathe in cold air, your body warms up that air.

I was personally useless for most of the Spring of 2020.

There is no shortage of stories about tech founders achieving face-melting wealth from startup success.

About 15 months ago, I left my full-time job as a machine learning team lead with the goal of doing independent / freelance data science consulting.

You can find me on GitHub, Twitter, LinkedIn, and email (hello at ethanrosenthal dot com) Talks Having suffered through years of dry physics talks during my academic days, I’m on a mission to give data science talks that are engaging and interesting while…

Having built machine learning products at two different companies with very different engineering cultures, I’ve made all of the following wrong assumptions All other data orgs do things like my company, so we’re doing fine.

In my previous posts in the “time series for scikit-learn people” series, I discussed how one can train a machine learning model to predict the next element in a time series.

A recent Twitter thread got me thinking back to the frustrations I felt working towards gaining non-research employment during the last year of my physics PhD program at Columbia University from 2014-2015.

How would you build a machine learning algorithm to solve the following types of problems?

Welcome!

We all know that Python has risen above its humble beginnings such that it now powers billion dollar companies.

In this post, I will walk through how to use my new library skits for building scikit-learn pipelines to fit, predict, and forecast time series data.

When I first started to learn about machine learning, specifically supervised learning, I eventually felt comfortable with taking some input $\mathbf{X}$, and determining a function $f(\mathbf{X})$ that best maps $\mathbf{X}$ to some known output value $y…

Update 7/8/2019: Upgraded to PyTorch version 1.0.

I’ve been making my way through the recently released Deep Learning textbook (which is absolutely excellent), and I came upon the section on Universal Approximation Properties.

After the long series of previous posts describing various recommendation algorithms using Sketchfab data, I decided to build a website called Rec-a-Sketch which visualizes the different algorithms’ recommendations.

To close out our series on building recommendation models using Sketchfab data, I will venture far from the previous [posts’]({{ ref “/blog/implicit-mf-part-2” >}}) factorization-based methods and instead explore an unsupervised, deep learning-based model.

In this post we’re going to do a bunch of cool things following up on the last post introducing implicit matrix factorization.

Last post I described how I collected implicit feedback data from the website Sketchfab.

– Zack de la Rocha tl;dr -> I collected an implicit feedback dataset along with side-information about the items.

Last post I talked about how data scientists probably ought to spend some time talking about optimization (but not too much time - I need topics for my blog posts!).

You’ve studied machine learning, you’re a dataframe master for massaging data, and you can easily pipe that data through a bunch of machine learning libraries.

In my last post, I described user- and item-based collaborative filtering which are some of the simplest recommendation algorithms.

I’ve written before about how much I enjoyed Andrew Ng’s Coursera Machine Learning course.

This is the final part in my series on going from PhD to Data Science (parts I and II).

Welcome to Part II of my journey from academic to industry data scientist.

The internet is awash with posts by former PhD students who have succesfully transitioned into data scientist roles in industry (see here, here, here, and tangentially here).

I think this post will probably conclude my Festival Chatter series on analyzing Bonnaroo tweets in Python (part 1, part 2, part 3).

In this series of posts (part 1, part 2), I have been showing how to use Python and other data scientist tools to analyze a collection of tweets related to the 2014 Bonnaroo Music and Arts Festival.

In my previous post, I wrote about how I collected tweets about the Bonnaroo Music and Arts Festival during the entirety of the festival.

It seems like summer music festivals get more and more popular every year.

As the title of the blog suggests, I would like to use this space to write about anything “data”-related that piques my interest.