A comprehensive resource for data scientists & ML engineers.
Nearly a year ago I announced the Machine & Deep Learning Compendium, a Google document that I have been writing for the last 4 years. The ML Compendium contains over 500-topics, and it is over 400 pages long.
Today I’m announcing that the Compendium is fully open. It is now…
A comprehensive resource on practically every topic for data science researchers
Edit: the compendium has its own website @ https://mlcompendium.com
In the last 3 years, I have been curating everything related, directly or indirectly, to machine-learning (ML), deep-learning (DL), Statistics, Probability, NLP, NLU, deep-vision, etc. I started curating a compendium because I wanted to expand the scope of my knowledge. I…
How algorithms are reused in nonstandard use cases.
Academics have been known to borrow ideas from nature and other fields while applying them in a slightly different way to new problems. These days, in data science, we see many ideas, technologies, and scientific advancements that are applied across the big three (NLP, Vision, Audio).
I would like to…
The following is an exhaustive list of companies in the world of MLOps Monitoring and their segmentation by personas, supported data type (tabular, image, audio, etc), product features (data-integrity, data-quality, health, drift, bias & fairness, XAI, etc), product focus (data-centric or pipeline-centric), total funding to date (Aug…
Text annotation tools are not just for creating datasets.
The very first thing that every researcher should do is to know their data and their domain. There are many ways to do it, from manually inspecting columns and rows, printing statistics, using pandas_summary, using feature selection methods, using Snorkel, visualizing data, interpreting your models, etc.
I argue that the…
An insight into requirements wants and needs, and common practices.
I have had the pleasure of working with several consultants, academic experts, freelancers, or individuals that work on a per-project basis, in various situations in the last few years. The following are specific use cases for when your organization actually needs them or when someone within the organization thinks that…
Or still a junior?
The following are my personal thought about what makes a senior data scientist (SDS) or in other words, what it takes for a junior to become one.
I guess some of these ideas could be generalized to other fields as well. …
Celebrating Springer-Nature release of 1000 free books.
The movement toward open-access has been given a tremendous push during the Covid-19 pandemic. One of the leading publications, SpringerNature, has increased the amount of free-book offerings to over 1000 books! It includes a huge amount of books, 122 in Computer Science alone!
Reasons for monitoring your model dependencies.
In my previous article “Monitor! Stop Being A Blind Data Scientist”, I mentioned the many use cases and the monumental importance of monitoring & alerts on our field, specifically from a data-science-researcher point-of-view. I looked at use-cases and reviewed several companies that provide varying solutions for this huge, but strangely…
The easy way.
DataFrame concatenations is an expensive action, especially in terms of processing time. Imagine having 12 Pandas DataFrames of varying sizes that you want to concatenate on the column axis, as seen in the following box.
df1 Shape: (24588, 31201)
df2 Shape: (24588, 1673)
df3 Shape: (24588, 5)
df4…
Ori Cohen
I hold a Ph.D. in CS. A lead DS for New Relic. Interested in ML, DL, NLP/U/G, BCI. I offer pro-bono advice to startups, contact me https://bit.ly/33Xcdwy
