[in case you missed it] Data Science Weekly - Issue 371
Issue #371 Dec 31 2020
Editor Picks
Machine learning is going real-time
After talking to machine learning and infrastructure engineers at major Internet companies across the US, Europe, and China, I noticed two groups of companies. One group has made significant investments (hundreds of millions of dollars) into infrastructure to allow real-time machine learning and has already seen returns on their investments. Another group still wonders if there’s value in real-time ML...
MLOps Tooling Landscape v2 (+84 new tools) - Dec '20
Last June, I published the post What I learned from looking at 200 machine learning tools. The post got some attention and I got a lot of messages from people telling me about new tools. I updated the old list to now include 284 tools. I’ll keep on updating the list as I find out about new tools...
Markov models and Markov chains explained in real life: probabilistic workout routine
You want to understand more about your optimal workout routine and even plan the next workout based on how you are normally structure it. So you realize that your workout routine can be modeled as a Markov chain. Since you pick the next exercise set based on the set you’ve done before, your workout routine follows the Markov assumption. It assumes the transition probability between each state only depends on the state you are in...
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Data Science Articles & Videos
How to create a simple Coronavirus dashboard specific to your country in R
I thought it would serve some people if I created a dashboard specific to my country (Belgium) and detailed the steps on how to build it...
Reverse Engineering Source Code of BioNTech/Pfizer SARS-CoV-2 Vaccine
In this post, we’ll be taking a character-by-character look at the source code of the BioNTech/Pfizer SARS-CoV-2 mRNA vaccine. Now, these words may be somewhat jarring - the vaccine is a liquid that gets injected in your arm. How can we talk about source code?...
Data-efficient image Transformers:
A promising new technique for image classification
We’ve developed a new method to train computer vision models that leverage Transformers, the breakthrough deep neural network architecture that has recently unlocked dramatic advances across many areas of AI...
Self-supervised self-supervision combining deep learning & probabilistic logic
In this paper, we propose Self-Supervised Self-Supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial "seed," S4 iteratively uses the deep neural network to propose new self supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments show that S4 is able to automatically propose accurate self-supervision and can often nearly match the accuracy of supervised methods with a tiny fraction of the human effort...
The definitive guide to AI monitoring
Learning from our work creating production visibility for teams across deep learning and machine learning use cases...
city roads: This website renders every single road within a city
This amazing little tool simply draws all streets in any city you want...
A few QA’s from the course F’20 Deep Learning
i’ve just finished teaching Deep Learning this semester together with Yann and Alfredo. the course was in a “blended mode”, this has resulted in more active online discussion among students, and indeed there were quite a few interesting questions posted... i enjoyed answering those questions, because they made me think quite a bit about them myself. of course, as usual i ended up leaving only a short answer to each, but i thought i’d share them here in the case any students in the future run into the same questions...
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
To facilitate the development of responsible machine learning models, we showcase dalex, a Python package which implements the model-agnostic interface for interactive model exploration. It adopts the design crafted through the development of various tools for responsible machine learning; thus, it aims at the unification of the existing solutions...
Literature of Deep Learning for Graphs
Here is a great repo containing papers on graph neural networks and other literature involving deep learning for graphs. Super useful for machine learning students....
Visualizing the Loss Landscape of a Neural Network
The loss landscape is a great tool for gaining intuition about stochastic gradient descent and how all of your choices regarding model architecture, batch size, etc. can affect the outcome of SGD. Check it out!...
Training*
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Data Science Resume Guide. This guide shows how to make your resume promote your best parts, what to leave out, how to tailor it to each job you want, as well as how to make your cover letter so good it can't be ignored!
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Jobs
Data Scientist - Apple Pay Analytics - NYC
You will play a key role improving the Apple Pay product experience. As a member of the analytics team you will be supporting a product function. You will partner with business owners, understand goals, craft KPIs and measure ongoing performance. You will initially engage with the product and engineering teams in ensuring that we have the appropriate instrumentation in place to deliver on these metrics. You will subsequently use advanced statistical, ML and analytical techniques to analyze product performance and identify key insights that inform product improvements and business strategy. The role requires a high degree of independence, ownership and collaboration working cross functionally across all levels of a highly matrixed organization...
Want to post a job here? Email us for details >> team@datascienceweekly.org
Training & Resources
A List of Best Papers from Top AI Conferences in 2020
Sharing a list of award-winning papers from this year's top conferences for anyone interested in catching up on the latest machine learning research before the end of the year...
NeurIPS tutorial on RL and optimization
Talk on RL as Black-box Optimization - interesting and well explained...
ML Visuals
ML Visuals is a new collaborative effort to help the machine learning community in improving science communication by providing free professional, compelling and adequate visuals and figures. Currently, we have over 100 figures (all open community contributions). You are free to use the visuals in your machine learning presentations or blog posts...
Books
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian