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Data Science Weekly Newsletter
January 13, 2022

Editor's Picks

  • Navigate Through the Current AI Job Market: A Retrospect
    Inspired by the fantastic talk focusing on career path doing AI research by Rosanne Liu and the amazing blog post on landing a job at top-tier AI labs by Aleksa Gordić, I want to share my recent experience to offer a more pragmatic perspective. The position specturm in the current AI industry can be roughly depicted in the figure below...
  • NeurIPS Anthology Visualization
    The NeurIPS conference has been around for more than 35 years, and interest in the fields of AI/ML is still rapidly growing. A diversification of interests has birthed many sub-fields within the fields, making it harder for novices and senior researchers alike to orient themselves and their work within the historical context of research published at NeurIPS. We created an interactive visualization to investigate the papers from the last 35+ years...

A Message From This Week's Sponsor

Live Webinar | How to Align AI & BI to Business Outcomes Wednesday, Jan 26 at 2PM ET (11AM PT) Get practical advice from Global 1000 data leaders at Visa, Cigna, Amazon, and HCL technologies on how they are aligning AI & BI toward business outcomes at their organizations.

Data Science Articles & Videos

  • Transformers
    Transformer models have become the go-to model in most of the NLP tasks. Many transformer-based models like BERT, ROBERTa, GPT series, etc are considered as the state-of-the-art models in NLP. While NLP is overwhelming with all these models, Transformers are gaining popularity in Computer vision also...While transformer models are taking over the AI field, it is also important to have a low-level understanding of these models. This blog aims to give an understanding of Transformer and Transformer based models. This includes the model components, training details, metrics and loss function, performance, etc...
  • The Use and Practice of Scientific Machine Learning [Video]
    Scientific machine learning (SciML) methods allow for the automatic discovery of mechanistic models by infusing neural network training into the simulation process. In this talk we will start by showcasing some of the ways that SciML is being used, from discovery of extrapolatory epidemic models to nonlinear mixed effects models in pharmacology. From there, we will discuss some of the increasingly advanced computational techniques behind the training process, focusing on the numerical issues involved in handling differentiation of highly stiff and chaotic systems. The viewers will leave with an understanding of how compiler techniques are being infused into the simulation stack to increasingly automate the process of developing mechanistic models...
  • Open source projects to contribute [Reddit Discussion]
    I'm at the point where I'd like to start contributing in a more meaningful way to the community. Does anyone have idea of good open source projects related to DL (maybe even classic machine learning) that are looking for contributors?...
  • Why Google Treats SQL Like Code and You Should Too
    For the past 2 years as a vendor working at Google, I’ve been observing the way Data Engineers at Google treat SQL the same way Software Engineers treat code. This winning mentality can be integrated into the data strategy of any company of any size. I’m going to walk through the ways that Google benefits from treating SQL like code and provide specific ways that all organizations can benefit from these principles...
  • Introduction to variational autoencoders
    Overview of the training setup for a variational autoencoder with discrete latents trained with Gumbel-Softmax. By the end of this tutorial, this diagram should make sense!...
  • Intro to Probabilistic Programming with PyMC [Video]
    In the last ten years, there have been a number of advancements in the study of Hamiltonian Monte Carlo and variational inference algorithms that have enabled effective Bayesian statistical computation for much more complicated models than were previously feasible...This talk will give an introduction to probabilistic programming with PyMC, with a particular emphasis on the how open source probabilistic programming makes Bayesian inference algorithms near the frontier of academic research accessible to a wide audience...


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Training & Resources


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