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Data Science Weekly Newsletter
January 17, 2019

Editor's Picks

  • What can neural networks learn?
    Neural networks are famously difficult to interpret. It’s hard to know what they are actually learning when we train them. Let’s take a closer look and see whether we can build a good picture of what’s going on inside...
  • Data Science Project Flow for Startups
    The aim of this post is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness...

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Data Science Articles & Videos

  • A New Approach to Understanding How Machines Think
    Neural networks are famously incomprehensible — a computer can come up with a good answer, but not be able to explain what led to the conclusion. Been Kim is developing a “translator for humans” so that we can understand when artificial intelligence breaks down...
  • Who are the best finishers in contemporary football (soccer)?
    The aim of the following analysis is to quantitatively evaluate finishing skill in football and to create an idea on who the best finishers are. By finishing skill here we will mean the ability of a player to transform shots into goals. As simple as that. It should be noted that finishing, as defined above, is different from goalscoring, which is potentially a broader and more complicated concept. The best finisher doesn’t necessarily mean the best goalscorer or the best forward. Let’s see what comes out!...
  • A Style-Based Generator Architecture for Generative Adversarial Networks
    We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature... An exciting property of style-based generators is that they have learned to do 3D viewpoint rotations around objects like cars. These kinds of meaningful latent interpolations show that the model has learned about the structure of the world...
  • Model Evaluation, Model Selection, and Algorithm Selection in ML
    The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages of each technique with references to theoretical and empirical studies...


  • Data Scientist - Uber for Business - NYC

    Uber for Business is on a path to revolutionize the way businesses manage their ground transportation needs. We need creative, quantitative thinkers with the ability to clearly synthesize and communicate insights from product data to accelerate us down this path. As a Data Scientist on Uber for Business you will work hand in hand with the Product, Marketing, Design, Sales and Engineering teams to keep product development data driven and informed. Candidates are expected to act with high levels of autonomy to guide their team’s roadmaps and build their data products....

Training & Resources

  • Qrash Course: Reinforcement Learning 101 & Deep Q Networks in 10 Minutes
    One of the first practical Reinforcement Learning methods I learned was Deep Q Networks, and I believe it’s an excellent kickstart to this journey. So allow me to walk you through the path I walked on when attempted to learn RL —including a “Hello World” exercise, which helped me more than I can explain...


  • Math for Machine Learning:
    Open Doors to Data Science and Artificial Intelligence

    From self-driving cars and recommender systems to speech and face recognition, machine learning is the way of the future. Would you like to learn the mathematics behind machine learning to enter the exciting fields of data science and artificial intelligence? There aren't many resources out there that give simple detailed examples and that walk you through the topics step by step.
    This book not only explains what kind of math is involved and the confusing notation, it also introduces you directly to the foundational topics in machine learning. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.
    Praise from students
    "Your book is by far the best I’ve found for understanding the derivations of machine learning algorithms. I love that you don’t skip steps and that you provide clear examples."--Robert H"... Link to preview of first 2 chapters and table of contents available here

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out
    our resources page

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