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Data Science Weekly Newsletter
October 10, 2019

Editor's Picks

  • Building AI to inform people's fashion choices
    An AI system that proposes easy changes to a person’s outfit to make it more fashionable. Our Fashion++ system uses a deep image-generation neural network to recognize garments and offer suggestions on what to remove, add, or swap. It can also recommend ways to adjust a piece of clothing, such as tucking in a shirt or rolling up the sleeves...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • Teaching AI to plan using language in a new open-source strategy game
    Facebook AI has developed a new method of teaching AI to plan effectively, using natural language to break down complex problems into high-level plans and lower-level actions. Our system innovates by using two AI models — one that gives instructions in natural language and one that interprets and executes them — and it takes advantage of the structure in natural language in order to address unfamiliar tasks and situations. We’ve tested our approach using a new real-time strategy game called MiniRTSv2, and found it outperforms AI systems that simply try to directly imitate human gameplay...
  • Streamlit launches open-source ML application development framework
    Streamlit, a new ML startup from industry veterans who worked at GoogleX and Zoox, launched today with a $6 million seed investment and a flexible new open-source tool to make it easier for machine learning engineers to create custom applications to interact with the data in their models...
  • Visualizing a Neural Net Controlling an Interplanetary Spacecraft Trajectory
    I made this visualization from a research paper I wrote, Neural Network Based Optimal Control: Resilience to Missed Thrust Events for Long Duration Transfers. It's made by simulating a Mars to Earth Trajectory and letting the neural network control the spacecrafts thrust and flight path angle. It's made using Matlab...
  • How to Evaluate the Logistic Loss and not NaN trying
    A naive implementation of the logistic regression loss can results in numerical indeterminacy even for moderate values. This post takes a closer look into the source of these instabilities and discusses more robust Python implementations...
  • Hamiltonian Graph Networks with ODE Integrators
    We introduce an approach for imposing physically informed inductive biases in learned simulation models. We combine graph networks with a differentiable ordinary differential equation integrator as a mechanism for predicting future states, and a Hamiltonian as an internal representation. We find that our approach outperforms baselines without these biases in terms of predictive accuracy, energy accuracy, and zero-shot generalization to time-step sizes and integrator orders not experienced during training...
  • How much Math & Stats do I need on my Data Science resume?
    Do I need a strong math background to pursue a career as a data scientist? We see a lot of questions like this. Its hard when you're trying to break into the field to know exactly how much math & stats you need. And, part of the reason for that is that it really depends...


In this webinar, Donald Miner - drawing upon his prior experience as a data scientist, engineer, and CTO - details the tracking of machine learning models in production to ensure model reliability, consistency, and performance into the future. Register here to attend or receive the recording
*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!


  • Principal Data Scientist - Next Caller - NYC

    Next Caller is searching for a data scientist with a contagious enthusiasm for data, a passion for exploratory problem solving, and a fascination with designing cutting-edge machine learning algorithms from scratch.
    As the Principal Data Scientist, you can expect to play an essential role in creating innovation and excellence in Next Caller’s Machine Learning-driven VeriCall platform for real-time call authentication and fraud prevention. In this role, you will be a core part of the Engineering Team, where you can expect trust, respect, collaboration, and humor as you engage in creative exploration within the department that is the lifeblood of our organization. The Next Caller Data Science Team uncovers unique insights that drive Next Caller’s real-time, API-based authentication services in a serverless AWS environment to clients in the financial services, telecommunications, healthcare, insurance, and travel and hospitality industries. We are seeking someone who is excited by the challenge of staying several steps ahead of fraudsters, and relishes the opportunity to tackle and solve problems that others think are just too difficult...
        Want to post a job here? Email us for details >>

Training & Resources


  • The Lady Tasting Tea:
    How Statistics Revolutionized Science in the Twentieth Century

    An insightful, revealing history of how mathematics transformed our world...
    "I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."...
    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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