• Hiring for Autonomy in Data Science Teams
  In our latest episode of the In Context Podcast, we're joined by Eric Colson, the Chief Algorithms Officer for Stitch Fix, an online subcription personal shopping service whose tech blog, Multithreaded, may have the coolest algorithms tour on the internet. In a fascinating discussion, he and Kathryn Hume analyze how Stitch Fix's engineering culture works, including what they value, what they look for in new hires, and how they’ve architected their platform to enable astounding success. You'll also hear about the critical role that autonomy plays in how Eric organizes his data science teams...

• Keras implementation of Image OutPainting
  Implementation of Painting Outside the Box: Image Outpainting paper...

• Data-mining medieval text reveals medically bioactive ingredients
  Medieval apothecaries used recipes with significant antibacterial properties, researchers say...

 
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• Embrace the noise: A case study of text annotation for medical imaging
  In this post we’ll discuss the recent paper TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays focusing on the best practices the paper exemplifies with regards to labeling text data for NLP...

• Good First Impressions According to Data Science
  Making a good first impression is hard. I made a model that can predict how good of an impression you are making based on a video clip submission...

• Ten Techniques Learned From fast.ai
  Right now, Jeremy Howard – the co-founder of fast.ai – currently holds the 105th highest score for the plant seedling classification contest on Kaggle, but he's dropping fast. Why? His own students are beating him. And their names can now be found across the tops of leaderboards all over Kaggle....

• L1: Tensor Studio — A playground for tensor computations
  An in-browser live-programming environment for differentiable tensor computations (written on top of TensorFlow.js)...

• AI-driven robot hand spent a hundred years teaching itself to rotate a cube
  A reinforcement-learning algorithm allows Dactyl to learn physical tasks by practicing them in a virtual-reality environment...

• When Recurrent Models Don't Need to be Recurrent
  We explore the trade-offs between recurrent and feed-forward models...

• Using Uncertainty to Interpret your Model
  Why uncertainty estimates are important in DL; Which types of uncertainty exist (data, model, measurement, label); Examples of how we can use uncertainty to interpret our model, and how we can use it for debugging...

• Deep Reinforcement Learning: Playing CartPole through Asynchronous Advantage Actor Critic (A3C) with tf.keras and eager execution
  In this tutorial we will learn how to train a model that is able to win at the simple game CartPole using deep reinforcement learning. We’ll use tf.keras and OpenAI’s gym to train an agent using a technique known as Asynchronous Advantage Actor Critic (A3C)...

• Multiply Two Matrices Using TensorFlow MatMul
  Learn how to multiply two matricies by using TensorFlow's matmul operation, via a screencast video and full tutorial transcript...

• The Hitchiker's Guide to PyTorch
  The rest of this document, based on the official MNIST example, is about grokking PyTorch, and should only be looked at after the official beginner tutorials. For readability, the code is presented in chunks interspersed with comments, and hence not separated into different functions/files as it would normally be for clean, modular code...

• Deep Learning: Which Loss and Activation Functions should I use?
  The purpose of this post is to provide guidance on which combination of final-layer activation function and loss function should be used in a neural network depending on the business goal...

• Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin
  "Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic"...
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