Data Science Weekly Newsletter - Issue 219

Issue #219

Feb 1 2018

Editor Picks
  • Lessons from Optics, The Other Deep Learning
    Imagine you’re an engineer, you’re given this deep learning net, and you’re asked to make it work better on a dataset. You might presume each of these layers is there for a reason. But as a field, we don’t yet have a common language to express these reasons. The way we teach deep learning is very different from the way we teach other technical disciplines...
  • Autonomous Driving using End-to-End Deep Learning: an AirSim tutorial
    In this tutorial, you will learn how to train and test an end-to-end deep learning model for autonomous driving using data collected from the AirSim simulation environment. You will train a model to learn how to steer a car through a portion of the Landscape map in AirSim using only one of the front facing webcams as visual input...
  • The Matrix Calculus You Need For Deep Learning
    This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed...

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Data Science Articles & Videos

  • Data Science Use Cases
    In this post, I’ll share some thoughts on how to decide which data science use cases to work on first, or next, based on what has been successful for me as a data science consultant helping companies, from Fortune 500s to startups. I like to separate the use case evaluation and selection process into three phases to make it a bit more manageable...
  • Recommending Animes Using Nearest Neighbors
    Recently, Myanimelist launched a dataset on Kaggle and I ended up making a simple recommender system with the data. In this post I’ll go over the procedure of making such a recommender based on properties like anime genre, rating, number of members reviewing the anime and share some results...
  • Andrew Ng officially launches his $175M AI Fund
    As the founder of the Google Brain deep learning project and co-founder of Coursera, Andrew Ng was one of the most recognizable names in the machine learning community when he became Baidu’s chief scientist in 2014. He left there in early 2017 and quickly launched a number of new AI projects, including the course and, a project that aims to bring AI to manufacturing companies. It turns out that what he was really working on, though, was his AI Fund...
  • Speedy Neural Networks for Smart Auto-Cropping of Images
    The ability to share photos directly on Twitter has existed since 2011 and is now an integral part of the Twitter experience. Today, millions of images are uploaded to Twitter every day. However, they can come in all sorts of shapes and sizes, which presents a challenge for rendering a consistent UI experience. The photos in your timeline are cropped to improve consistency and to allow you to see more Tweets at a glance. How do we decide what to crop, that is, which part of the image do we show you?...
  • The UX of AI
    Using Google Clips to understand how a human-centered design process elevates artificial intelligence...
  • DataFramed: Podcast Series from DataCamp
    Last week, we launched a podcast called DataFramed, in which I interview industry thought leaders, such as Hilary Mason (FF Labs), Chris Volinsky (AT&T), Claudia Perlich (Dstillery), Dave Robinson (DataCamp, previously Stack Overflow) and Robert Chang (Airbnb)...



  • Data Scientist - Raise Marketplace - New York or Chicago
    Raise, a leading retail payments company and the world’s largest gift card marketplace, connects consumers to buy discounted gift cards or sell their unwanted cards for cash.

    Raise Data Scientists are a critical component of the cross-functional squads that make up the Raise Technology organization, reporting directly to the Director of Analytics. As a Data Scientist at Raise, you are responsible for delivering data informed insights to a given Technology Squad, while providing expertise in data collection, experiment design, and interpretation of statistics. You will be responsible for creating, analyzing and optimizing reports that help to drive critical product decisions...

Training & Resources

  • Create A TensorFlow Placeholder Tensor
    Learn how to create A TensorFlow Placeholder Tensor and then when it needs to be evaluated pass a NumPy multi-dimensional array into the feed_dict so that the values are used within the TensorFlow session...
  • Observable is a better way to code
    Discover insights faster and communicate more effectively with interactive notebooks for data analysis, visualization, and exploration...
  • Exploring Supervised Machine Learning Algorithms
    The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python’s scikit-learn library and then apply this knowledge to solve a classic machine learning problem...



  • Seven Databases in Seven Weeks:
    A Guide to Modern Databases and the NoSQL Movement

    "A book that tries to cover multiple database is a risky endeavor, a book that also provides hands on on each is even riskier but if implemented well leads to a great package. I loved the specific exercises the authors covered. A must read for all big data architects who don’t shy away from coding..."

    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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