Tag: machine learning

I invited Dr. Charles Earl for this episode of my podcast “Job Interview” to talk about racial discrimination at the workplace and fairness in machine learning. Dr. Charles Earl is...

NDR is a family of machine learning/data science conferences. Their next conference will be held online on May, 28 and the agenda looks great. Now, I’m not super objective here,...

HBR published an opinion post by Andrew Burt, called “The AI Transparency Paradox.” This post talks about the problems that were created by tools that open up the “black box”...

My teammate, Charles Earl has recently attended the Conference on Fairness, Accountability, andTransparency (FAT*). The conference site is full of very interesting material, including proceedings and video recording of lectures...

Overfitting is a situation in which a model accurately describes some data but not the phenomenon that generates that data. Overfitting was a huge problem in the good old times,...

My stand on learning data science is known: I think that learning “data science” as a career move is a mistake. You may read this long rant of mine to...

It’s such a joy to work with smart and interesting people. My teammate, Charles Earl, wrote a post about machine learning and poverty. It’s not short, but it’s worth reading....

Many years ago, I tried to build something that today would have been called “Google Trends for Pubmed”. One thing that I’ve found during that process was how the emergence...

I developed an anomaly detection system for Automattic internal dashboard. When presenting this system (“When good enough is just good enough”), I used to tell that in our particular case,...

If you are a data scientist, I am sure you wondered whether deep neural networks will replace you at your job one day. Every time I read about reports of...

I went through my Machine Learning tag feed. Suddenly, I stumbled upon a pie chart that looked so terrible, I was sure the post would be about bad practices in...

I really recommend reading this (longish) post by Tom Breur called “Data Dredging” (and following his blog. The post is dedicated to overfitting – the most scaring problem in machine...

Frequently, training a machine learning model in a single session is impossible. Most commonly, this happens when one needs to update a model with newly obtained observations. The generic term...

My today’s talk at PyCon Israel in a post format.

On June 12th, I’ll be talking about anomaly detection and future forecasting when “good enough” is good enough. This lecture is a part of PyCon Israel that takes place between...