Data Science Weekly Newsletter - Issue 403

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...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • 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...
  • 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...
  • 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!...
 
 

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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

 
  • 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
 
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