Data Science Weekly Newsletter - Issue 228

Issue #228

Apr 5 2018

Editor Picks
  • Turning a MacBook into a Touchscreen with $1 of Hardware
    We turned a MacBook into a touchscreen using only $1 of hardware and a little bit of computer vision. The proof-of-concept, dubbed “Project Sistine” after our recreation of the famous painting in the Sistine Chapel, was prototyped in about 16 hours...
  • On Machine Learning and Programming Languages
    While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modelling language (like PyTorch). We’d like to ask – are new ML-tailored languages required, and if so, why? More importantly, what might the ideal ML language of the future look like?...

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Data Science Articles & Videos

  • Data Science at the NIH and in healthcare
    The National Institutes for Health (NIH) are on an ambitious effort to harness advances in data science, machine learning, and artificial intelligence (AI) to support programs like the Precision Medicine, Cancer Moonshot, and Brain Initiatives. To accelerate progress, the NIH made a call to the public for a Request for Information (RFI)on the proposed Strategic Plan on Data Science. I submitted my letter and a number of people asked me to make my letter public...
  • Apple Hires Google’s A.I. Chief
    Apple has hired Google’s chief of search and artificial intelligence, John Giannandrea, a major coup in its bid to catch up to the artificial intelligence technology of its rivals...
  • Wildbook: Software to Combat Extinction
    Wildbook blends structured wildlife research with artificial intelligence, citizen science, and computer vision to speed population analysis and develop new insights to help fight extinction. Here is our vision in one minute...
  • Designing a Data Visualization Dashboard Like It was a Game
    Here, I’ll focus in on specific attributes of charts and analytical applications that, if viewed as game elements, could enable new approaches for how to design, navigate and manipulate analytical applications such as data dashboards. Some of the mechanics I think are most adaptable from the discourse of games into the discourse of data visualization are: maps, items, crafting and guild halls...
  • Neural network-named tomatoes you won’t find at the farmer’s market
    Darren Abbey, who runs the blog The Biologist Is In, breeds new tomatoes and needs to name them from time to time. He gave me a list of 11,629 existing names, ranging from the highly experimental (Rts-119) to the classic (Sungold). I used these names to train a recurrent neural network, which is a kind of algorithm that learns patterns that let it imitate the data it sees. Could it learn to produce plausible tomato names?...
  • Evolving Alien Corals
    A research project simulating the evolution of virtual corals. Corals are grown in underwater environments containing light and current flow and are evolved with a genetic-algorithm...



  • Data Scientist - elasticiti - NYC
    You will work on stimulating projects related to pre and post-sale media plan optimization. You will have the benefits of a broad range of enterprise-grade analytics tools as well as the space to introduce other technologies and methods. This is a new initiative where you will be setting the technical foundation and methodologies that will be employed for years to come.

    This is a full-time role working directly for one of the largest media companies in the industry here in New York City...

Training & Resources

  • TensorFlow.js
    TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. A) Develop ML in the Browser - Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API. B) Run Existing models - Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser. C) Retrain Existing models - Retrain pre-existing ML models using sensor data connected to the browser, or other client-side data...
  • Introducing TensorFlow Hub:
    A Library for Reusable Machine Learning Modules in TensorFlow

    TensorFlow Hub is a platform to publish, discover, and reuse parts of machine learning modules in TensorFlow. By a module, we mean a self-contained piece of a TensorFlow graph, along with its weights, that can be reused across other, similar tasks. By reusing a module, a developer can train a model using a smaller dataset, improve generalization, or simply speed up training. Let’s look at a couple examples to make this concrete...



  • Data Science from Scratch: First Principles with Python

    "It does three things superbly: covers the basic low level tools of a data scientist (the "from scratch" part), gives a great overview of useful Python programming examples for those new to Python, and gives an amazingly succinct yet high level overview of the mathematics and statistics required for data science..."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian
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