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Data Science Weekly Newsletter
August 24, 2017

Editor's Picks

  • Writing with the machine: Experimenting with RNNs in your text editor
    Building this felt like playing with Lego, except instead of plastic bricks, I was snapping together conveniently-packaged blocks of human intellect and effort. One block: a recurrent neural network, fruit of the deep learning boom, able to model and generate sequences of characters with spooky verisimilitude. Snap! Another block: a powerfully extensible text editor. Snap! Together: responsive, inline “autocomplete” powered by an RNN trained on a corpus of old sci-fi stories...
  • PyTorch or TensorFlow?
    This is a guide to the main differences I’ve found between PyTorch and TensorFlow. This post is intended to be useful for anyone considering starting a new project or making the switch from one deep learning framework to another. The focus is on programmability and flexibility when setting up the components of the training and deployment deep learning stack. I won’t go into performance (speed / memory usage) trade-offs...
  • The Elvis Presley Jukebox
    Elvis-head heatmap jukebox of his music... We found the approximate tempo for a reasonable collection (close to 400) of Elvis Presley numbers from this youtube playlist. Each grid below is a song. A brighter blue indicates a faster tempo & a lighter blue indicates a slower tempo...

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Cheers, Hannah & Sebastian.

Data Science Articles & Videos

  • Learning things we already know about stocks
    This example groups stocks together in a network that highlights associations within and between the groups using only historical price data. The result is far from ground-breaking; you can already guess the output. For the most part, the stocks get grouped together into pretty obvious business sectors. Despite the obvious result, the process of teasing out latent groupings from hisoric price data is interesting. That’s the focus of this example...
  • How to build an image recognizer in R using just a few images
    Training an image recognition system requires LOTS of images — millions and millions of them. It involves feeding those images into a deep neural network, and during that process the network generates "features" from the image. These features might be versions of the image including just the outlines, or maybe the image with only the green parts. With enough of these "features", you could use them in a traditional machine learning model to classify the images, or perform other recognition tasks. But if you don't have millions of images, it's still possible to generate these features from a model that has already been trained on millions of images...
  • Animating a spinner using ggplot2 and ImageMagick
    I’m quite pleased with myself for being able to use polar coordinates to create the spinner and arrow. ggplot works surprisingly well in polar coordinates once you figure them out; almost everything people have said about them online is confused and the doc itself assumes you’re a bit more of a ggplotter and geometer than me...
  • A Brief Survey of Deep Reinforcement Learning
    In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policybased methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic...


  • Senior Data Scientist - Trace3 - Irvine, CA
    Do you enjoy solving computer vision problems such as Optical Character Recognition (OCR), object detection and image classification? Do you love applying new state of the art machine learning and deep learning algorithms? If yes, consider joining Trace3. At Trace3, we bridge the gap between traditional IT and business stakeholders to partner with organizations to achieve success in the Big Data World. Trace3's best and brightest people make this happen. If you are someone who wants to move the needle in this industry, please apply...

Training & Resources

  • Scikit-learn hyperparameter search wrapper
    Scikit-optimize provides a drop in replacement for GridSearchCV, which utilizes Bayesian Optimization where a predictive model reffered to as "surrogate" is used to model the search space and utilized in order to arrive at good parameter values combination as soon as possible...
  • Cross-compiling TensorFlow for the Raspberry Pi
    I love the Raspberry Pi because it’s such a great platform for software to interact with the physical world. TensorFlow makes it possible to turn messy, chaotic sensor data from cameras and microphones into useful information, so running models on the Pi has enabled some fascinating applications, from predicting train times, sorting trash, helping robots see, and even avoiding traffic tickets! It’s never been easy to get TensorFlow installed on a Pi though...


  • Thinking Statistically
    "A truly excellent read and far more fun than a book about statistics has any right to be..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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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