Data Science Org Design for Startups While there is plenty of good advice on making ML work and making a career as a Data Scientist - I think very little discussion happens on the organization design for Data Science itself. This blog will hopefully help folks not just build their team, but also understand the ecosystem from which they are hiring. Organization Design is determined by these 3 broad categories:

Who is this for? Practitioners who are looking to level up their game in Deep Learning Why Do We Need Instructions on How to Read a Deep Learning Paper? Quantity: There are more papers than we can humanly read even within our own niche. For instance, consider EMNLP - which is arguably the most popular Natural Language Processing conference selects more than 2K papers across a variety of topics. And NLP is just one area!

Hello! If we are meeting for the first time, a short version of my story so far: After doing research engineering for almost 4 years across startups and a BigCo, I joined as an early machine learning engineer at Verloop.io - a B2B startup that makes customer support automation SaaS in 2019. I was there till April 2021. We were directly responsible for most Natural Language Processing needs within the business.

Update, September 2021: This guide is a little outdated, but not obsolete. I no longer work at Verloop.io. Preparation Guide I’ve been an early Machine Learning Engineer at Verloop.io for almost 1.5 years, primarily working on NLP problems and now more in an Engineering Manager-ish role. This is the guide which I sometimes send to our candidates after they submit the Programming Challenge. If a candidate has relevant open source code sample, specially to other repositories we may choose to waive off the Programming Challenge completely.

Algebra, Topology, Differential Calculus, andi Optimization Theory For Computer Science and Machine Learning https://www.cis.upenn.edu/~jean/math-deep.pdf Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf http://d2l.ai/chapter_appendix_math/index.html

Mayank asked on Twitter: Some ideas/papers/tools on monitoring models in production. A use case would be say a classification task over large inputs. I want to visualise how are the predicted values or even confidence scores vary over time? (paraphrased) Quick Hacks pandas-profiling If you are logging confidence scores, you can begin there. The quickest hack is to visualize with pandas-profiling: https://github.com/pandas-profiling/pandas-profiling/ Rolling means Calculate rolling aggregates (e.g. mean, variance) of your confidence scores.

The Silent Rise of PyTorch Ecosystem While Tensorflow has made peace with Keras as it’s high level API and mxNet now support Gluon — PyTorch is the bare matrix love. PyTorch has seen rapid adoption in academia and all the industrial labs that I have spoken to as well. One of the reasons people (specially engineers doing experiments) like PyTorch is the ease of debugging. What I don’t like about PyTorch is it’s incessant requirement of debugging because of inconsistent dimensions problems.

A Getting Started Guide Let us get our facts straight, shall we? I am writing from my non-existent but probably relevant experience. I worked in a Machine Learning role at Samsung Research, Bengaluru. It is only 1 of the 4 research enterprises which hire Machine Learning researchers from Indian colleges — the other being Microsoft, Xerox, and IBM Watson. I am now in a even more Computer Vision focused role for a small enterprise tech company.