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Data Science Weekly Newsletter
October 29, 2020

Editor's Picks

  • Coronavirus: Why You Must Act Now
    With everything that’s happening about the Coronavirus, it might be very hard to make a decision of what to do today. Should you wait for more information? Do something today? What? Here’s what I’m going to cover in this article, with lots of charts, data and models with plenty of sources: How many cases of coronavirus will there be in your area? What will happen when these cases materialize? What should you do? When?...
  • AlphaGo - The Movie | Full Documentary
    Four years ago, AlphaGo and the legendary Lee Sedol took on an unprecedented challenge. To celebrate the landmark anniversary, we’re releasing AlphaGo The Movie for free on YouTube ...

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Data Science Articles & Videos

  • Epidemic Calculator
    At the time of writing, the coronavirus disease of 2019 remains a global health crisis of grave and uncertain magnitude. To the non-expert (such as myself), contextualizing the numbers, forecasts and epidemiological parameters described in the media and literature can be challenging. I created this calculator as an attempt to address this gap in understanding...
  • Real Time COVID-19 Tracking
    In the absence of widespread testing, we need to rely in EpiQuery (and ILINet, the federal CDC version covering all 50 states) to understand the likely growth of the COVID-19 outbreak...
  • Visualizing Neural Networks with the Grand Tour
    This visualization shows the behavior of the final 10-dimensional layer of a neural network as it is trained on the MNIST dataset. With this technique, it is possible to see interesting training behavior...
  • Does On-Policy Data Collection Fix Errors in Off-Policy Reinforcement Learning?
    In this blog post, we will dive deep into analyzing a central and underexplored reason behind some of the problems with the class of deep RL algorithms based on dynamic programming, which encompass the popular DQN and soft actor-critic (SAC) algorithms – the detrimental connection between data distributions and learned models...
  • Analysis of Hyper-Parameters for Small Games:
    Iterations or Epochs in Self-Play?

    There has been surprisingly little research on design choices for hyper-parameter values and loss-functions, presumably because of the prohibitive computational cost to explore the parameter space. In this paper, we investigate 12 hyper-parameters in an AlphaZero-like self-play algorithm and evaluate how these parameters contribute to training. We use small games, to achieve meaningful exploration with moderate computational effort...
  • Higher accuracy on vision models with EfficientNet-Lite
    Today, we are excited to announce EfficientNet-Lite (GitHub, TFHub), which runs on TensorFlow Lite and designed for performance on mobile CPU, GPU, and EdgeTPU. EfficientNet-Lite brings the power of EfficientNet to edge devices and comes in five variants, allowing users to choose from the low latency/model size option (EfficientNet-Lite0) to the high accuracy option (EfficientNet-Lite4)...


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  • Head of Data Science - Tessian - London, United Kingdom

    Our mission is to secure the Human Layer. This involves deploying near real-time machine learning models at massive scale to some of the world’s largest organisations to keep their most sensitive data private and secure. To do this, we're looking for an inspiring Head of Data Science ready to lead and grow our Data Science team, who is excited about the opportunities and challenges that come with building and deploying real-time production models.
    Find out more about life as a Tessian Engineer...

        Want to post a job here? Email us for details >>

Training & Resources

  • The Dataset of Epidemiological Case Reports for COVID-19
    This repository contains a dataset (named ECR-COVID-19) of epidemiological case reports with entity labeling which can be used for information extraction. The motivation of creating and contributing the dataset is to trigger the research on epidemiologic investigation analysis and automation...


  • Data Science in Production: Building Scalable Model Pipelines with Python
    This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production....
    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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