Data Science Weekly Newsletter - Issue 177

Issue #177

April 13 2017

Editor Picks
  • A Peek at Trends in Machine Learning
    Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years? The results are fairly fun, so I thought I’d post...
  • Why Momentum Really Works
    Momentum can be understood far more precisely if we study it on the right model. One nice model is the convex quadratic. This model is rich enough to reproduce momentum’s local dynamics in real problems, and yet simple enough to be understood in closed form. This balance gives us powerful traction for understanding this algorithm...

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Data Science Articles & Videos

  • Promoting Positive Climate Change Conversations via Twitter
    For my final project of the Metis Data Science program, I investigated the climate change conversations taking place on Twitter in March 2017. The 1 million tweets that I looked at were a snapshot in time - many users talked about EPA head Scott Pruitt’s denial that CO2 causes global warming, or the critical condition of Australia’s Great Barrier Reef. Sub-communities were also apparent within the greater conversation, including a group of climate change deniers who stood out from the rest...
  • RNN First Result - Bach Chorales
    This is the first result of my RNN trained with Bach Chorales. I'm using some vst to spice up the sound but chords and rhythm are completely generated by the net...
  • Unsupervised sentiment neuron
    We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews...
  • Federated Learning: Collaborative Machine Learning without Centralized Training Data
    Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we're introducing an additional approach: Federated Learning...
  • How to fake a sophisticated knowledge of wine with Markov Chains
    To the untrained (like me), wine criticism may seem like an exercise in pretentiousness. It may seem like anybody following a set of basic rules and knowing the proper descriptors can feign sophistication (at least when it comes to wine). In this post, we will be exploiting the formulaic nature of wine reviews to automatically generate our own reviews that appear (at least to the untrained) to be legitimate...
  • CycleGAN
    Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more...


  • Data Scientist - NBA - New York

    The NBA's Team Marketing and Business Operations ("TMBO") group is a unique in-house consulting arm within the NBA league office that strives to drive best practices and innovation across all 64 NBA, WNBA and NBA Development League teams. The primary focus for this fast paced and collaborative department is on all aspects of business operations, including ticket sales and service, sponsorship, marketing, digital, analytics, and data strategy.

    The Data Scientist role will be a technical expert within TMBO in all matters surrounding statistical analysis, data manipulation and interpretation, and process automation. You will be a thought leader, tasked with the responsibility to leverage the NBA's various internal data sources to create new and innovative analytical products and outputs to inform league executives about the state of team businesses...

Training & Resources

  • Python TensorFlow Tutorial – Build a Neural Network
    This introductory tutorial to TensorFlow will give an overview of some of the basic concepts of TensorFlow in Python. These will be a good stepping stone to building more complex deep learning networks, such as Convolution Neural Networks and Recurrent Neural Networks, in the package...
  • Explanation of Neural Turing Machines
    I haven't found a good resource for people with a technical background who are unfamiliar with the more advanced concepts and are looking for someone to fill them in. This is my attempt to bridge that gap...



  • Bayes Theorem: A Visual Introduction For Beginners

    "This book takes what can be a daunting and complex subject and breaks it down with a series of easy to follow examples which buildup to deliver a great overall explanation of how to use Bayes Theorem for basic analysis and even off-the-cuff critical thinking"...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S. Looking to hire a Data Scientist? Find an awesome one among our readers! Email us for details on how to post your job :) - All the best, Hannah & Sebastian
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