• Amazon's AI Fashionista Works Out What Style Suits You
  The robot fashion guru: Amazon's AI uses machine-learning to work out what suits you - and it can even create new styles from scratch...

• My Year at Brain
  I've spent the past year doing the Google Brain Residency Program. It's been a great opportunity for the first year out of my PhD, and in this blog post I'll describe what the residency was like, what I worked on while here, and what I'm doing next....

• Advice for non-traditional data scientists
  I am now a Senior R Developer at a survey company called Crunch.io. When I started, I honestly didn’t have any particular skills or capacity which would have made data science a good career choice. I studied philosophy in undergrad, and while I had done a bit of statistics, it wasn’t something I would have said I was comfortable with. All I really had was an interest and the capacity to learn new things. If you’re in a similar boat, here is some advice about the process...

 
Create D3 Data Visualizations As Fast As You Can Sketch

You need to create a D3.js data visualization to communicate your insights. But... #d3BrokeAndMadeArt! This time, your data join appears to have broken and the JavaScript console shows an error you don't recognize. Last time, you got stuck trying to figure out how to make axes that didn't look like 3rd graded made them. It makes you want to strangle D3 with your bare hands. Just how steep does the D3 learning curve need to be?!
What if you could learn and master D3 quickly and deeply?
Great news! - You can ... Check out DashingD3js.com Screencasts today!
   

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• Data Alone Isn’t Ground Truth … and why you should always carry a healthy dose of skepticism in your back pocket.
  I saw a chart the other day that highlighted the importance of bringing skepticism to any data analyses and visualizations we encounter. This post isn’t a dissertation, and it necessarily does not address the topic fully. But I think there are some good point I can make on the topic, and if it sparks a good conversation that I think it’s served a good purpose...

• How AI Fits Into Your Data Science Team
  In their HBR Big Idea feature, Erik Brynjolfsson and Andrew McAfee argue that AI and machine learning will soon become “general-purpose technologies,” as significant as electricity or the internal combustion engine. They represent a landmark change in our technical capabilities and will power the next wave of economic growth...

• Who migrates in and out of New York City
  A new data visualization from Chris Whong shows demographic trends in the city since 1935...

• Tensor Flow App for Cassava Disease Diagnosis
  Our app on AI (deep learning) for cassava diseases got its first field outing...

• Mario Kart 64 - NEAT Algorithm
  Neural Net evolved to play Mario Kart 64, requiring only small computing power...

• Background removal with deep learning
  Background removal is a task that is quite easy to do manually, or semi manually (Photoshop, and even Power Point has such tools) if you use some kind of a “marker” and edge detection, see here an example. However, fully automated background removal is quite a challenging task, and as far as we know, there is still no product that has satisfactory results with it, although some do try...

• How well do Google and Microsoft recognize speech across dialect, gender and race?
  If you’ve been following my blog for a while, you may remember that last year I found that YouTube’s automatic captions didn’t work as well for some dialects, or for women. The effects I found were pretty robust, but I wanted to replicate them for a couple of reasons...

• Machine Learning in Production:
  From trained models to prediction servers
  After days and nights of hard work, going from feature engineering to cross validation, you finally managed to reach the prediction score that you wanted. Is it over? Well, since you did a great job, you decided to create a microservice that is capable of making predictions on demand based on your trained model. Let’s figure out how to do it. This article will discuss different options and then will present the solution that we adopted at ContentSquare to build an architecture for a prediction server...

• Deep Learning (DLSS) and Reinforcement Learning (RLSS) Summer School, Montreal 2017
  Videos Available: The Deep Learning Summer School (DLSS) is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research...

• Deploying your Keras model using Keras.JS
  After we explored our first option of running a deep learning model on the server side, let’s consider running it in the browser...

• Object detection: an overview in the age of Deep Learning
  There’s no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. One of the problems we’re most interested in and have worked on a bunch is object detection. Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem, meaning there’s still much room for improvement. Before getting into object detection, let’s do a quick rundown of the most common problems in the field...

• 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