Data Science Weekly Newsletter - Issue 408

Issue #376

Feb 04 2021

Editor Picks
  • "Everyone wants to do the model work, not the data work":
    Data Cascades in High-Stakes AI

    Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA. We define, identify, and present empirical evidence on Data Cascades---compounding events causing negative, downstream effects from data issues---triggered by conventional AI/ML practices that undervalue data quality...
  • 2011: DanNet triggers deep CNN revolution
    In 2021, we are celebrating the 10-year anniversary of DanNet..the first pure deep convolutional neural network (CNN) to win computer vision contests...From 2011 to 2012 it won every contest it entered, winning four of them in a row (15 May 2011, 6 Aug 2011, 1 Mar 2012, 10 Sep 2012), driven by a very fast implementation based on graphics processing units (GPUs). Remarkably, already in 2011, DanNet achieved the first superhuman performance in a vision challenge, although compute was still 100 times more expensive than today. In July 2012, our CVPR paper on DanNet hit the computer vision community. The similar AlexNet (citing DanNet) joined the party in Dec 2012...Today, a decade after DanNet, everybody is using fast deep CNNs for computer vision...
  • Data science notebooks | 2020 Review
    We dug into the data to learn more about the current state of a vital part of the data science ecosystem: the notebooks...This article consists of 5 key sections: 1) First, we explore key stats and trends of Jupyter notebooks on GitHub, 2) Second, we double down on popular Python libraries and show you what libraries to add to your toolkit for plotting, ML, NLP, and other use cases, 3) Last, we look at search trends from Google & YouTube, 4) Data sources and how you can build on top of them, and 5) Conclusion & ideas for future research...

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Data Science Articles & Videos

  • Machine Learning Street Talk #40: Adversarial Examples with Dr. Nicholas Carlini, Dr. Wieland Brendel, and Florian Tramèr [YouTube Video]
    Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. there's good reason to believe neural networks look at very different features than we would have expected...Adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans...Adversarial examples don't just affect deep learning models. A cottage industry has sprung up around Threat Modeling in AI and ML Systems and their dependencies. Joining us this evening are some of currently leading researchers in adversarial examples...
  • Reinforcement Learning At Facebook with Jason Gauci [Podcast Episode]
    Jason has worked on YouTube recommendations. He was an early contributor to TensorFlow the open-source machine learning platform. His thesis work was cited by DeepMind...But what I find so fascinating with Jason is he recognized this problem that was being solved the wrong way and set out to find a solution to it...The problem was making recommendations, like on Amazon, people who bought this book might like that book. He didn’t exactly know how to solve the problem, but he knew it could be done better...So that’s the show today. Jason is going to share his story...
  • Machine learning accelerated computational fluid dynamics (CFD) [PDF]
    Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows...
  • A Complete Guide to Revenue Cohort Analysis
    A Cohort Analysis is an extremely useful tool that allows you to gather insights pertaining to customer churn, lifetime value, product engagement, stickiness, and more...Cohort analyses are especially useful for improving user onboardings, product development, and marketing tactics. What makes cohort analyses so powerful is that they’re essentially a 3-dimensional visualization, where you can compare a value/metric across different segments over time...By the end of this article, you’ll learn how to create something like this...
  • Data Observability: Building Data Quality Monitors Using SQL
    In this article series, we walk through how you can create your own data observability monitors from scratch, mapping to five key pillars of data health. Part 1 of this series was adapted from Barr Moses and Ryan Kearns’ O’Reilly training, Managing Data Downtime: Applying Observability to Your Data Pipelines, the industry’s first-ever course on data observability...
  • spaCy 3.0 release notes
    v3.0.0: Transformer-based pipelines, new training system, project templates, custom models, improved component API, type hints & lots more...
  • A Data Scientist’s Guide to Lazy Evaluation with Dask
    Dask is an open-source framework that enables parallelization of Python code...We talk a lot about Dask and parallel computing, but sometimes we don’t do enough to explain the concepts that make it possible. Read on to learn how lazy evaluation works, how Dask uses it, and how it makes parallelization not only possible but easy!...



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Training & Resources

  • Weave.jl - Scientific Reports Using Julia
    This is the documentation of Weave.jl. Weave is a scientific report generator/literate programming tool for Julia. It resembles Pweave, knitr, R Markdown, and Sweave...
  • Full Stack Deep Learning - Lecture #1
    In this video, we discuss the fundamentals of deep learning. We will cover artificial neural networks, the universal approximation theorem, three major types of learning problems, the empirical risk minimization problem, the idea behind gradient descent, the practice of back-propagation, the core neural architectures, and the rise of GPUs...



  • 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
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