• Andrew Ng: Artificial Intelligence is the New Electricity
  On Wednesday, January 25, 2017, Baidu chief scientist, Coursera co-founder, and Stanford adjunct professor Andrew Ng spoke at the Stanford MSx Future Forum. The Future Forum is a discussion series that explores the trends that are changing the future. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry...

• What was AI in 2017?
  Very interesting summary of "2017 AI Tech Talk Series" plus links to all the talk videos...

• Fighting Gerrymandering
  I had seen a lot of work using data science to prove that an existing map had been egregiously gerrymandered. But I had seen less work using data science to draw an optimally fair map. The challenge with drawing an optimally fair map, however, is that reasonable people disagree about what makes a map fair...

 
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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• Slack Hopes Its AI Will Keep You from Hating Slack
  The fastest-growing business app is relying on machine-learning tricks to fend off a deluge of messages—as well as competition from Facebook and Microsoft...

• Building The Analytics Team At Wish
  Very few people believed that a large business could be built from selling low priced products. Using data, Wish has been able to test and challenge these assumptions. Being data driven was in the company DNA. But from the company’s massive growth were huge growing pains on the analytics side...

• Car Back!: A video car detector for cyclists
  My idea was formed to create a tool that alerts a rider when a car is approaching from behind. For those that don't know, "Car Back!" is what one cyclist will shout to another to alert them of an approaching car from behind - so I took this name for my project. My vision is to be able to attach a camera to the back of my bike, near the seat which captures video in real time and alerts of any cars that are approaching from behind. The alert would be an audio cue that is played in one of the apps that is already running -- Strava, Spotify, or Audible as examples...

• Understanding Learning Rates and How It Improves Performance in Deep Learning
  This post is an attempt to document my understanding on the following topic: What is the learning rate? What is it’s significance? How does one systematically arrive at a good learning rate? Why do we change the learning rate during training? How do we deal with learning rates when using pretrained model?...

• Exploring Recommendation Systems
  While we commonly associate recommendation systems with e-commerce, their application extends to any decision-making problem which requires pairing two types of things together. To understand why recommenders don’t always work as well as we’d like them to, we set out to build some basic recommendation systems using publicly available data...

• Consecutive category morphing of GANs generated images
  Video of morphing between ImageNet categories with SN-GAN...

• To a Poem is a Bott the Stranger
  Code is Poetry. This is part of the WordPress philosophy. As a coder and a poet, I have always loved this phrase. I decided to turn this phrase around and ask, Can I make poetry with code? Could I make a bot that could write original poetry? I created an experiment to find out....

• How to solve 90% of NLP problems: a step-by-step guide
  After leading hundreds of projects a year and gaining advice from top teams all over the United States, we wrote this post to explain how to build Machine Learning solutions to solve problems like... a) Identifying different cohorts of users/customers (e.g. predicting churn, lifetime value, product preferences), b) Accurately detecting and extracting different categories of feedback (positive and negative reviews/opinions, mentions of particular attributes such as clothing size/fit…), and c) Classifying text according to intent (e.g. request for basic help, urgent problem)...

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• Transfer A 1-D Tensor To A Vector Using TensorFlow
  Learn how to transfer a 1-D Tensor to a Vector using the TensorFlow squeeze transformation to remove the dimension of size 1 from the shape of the tensor...

• Machine Learning Conferences in 2018
  Listing 240 business and academic events worldwide...

• Tutorial: How We Productized Bayesian Revenue Estimation with Stan
  It's easy to react to the wrong signals and waste money on less successful ad campaigns. Luckily, Bayesian inference allows us to make justified decisions on a granular level by modeling the variation in the observed data...

• Seven Databases in Seven Weeks:
  A Guide to Modern Databases and the NoSQL Movement
  "A book that tries to cover multiple database is a risky endeavor, a book that also provides hands on on each is even riskier but if implemented well leads to a great package. I loved the specific exercises the authors covered. A must read for all big data architects who don’t shy away from coding..."...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...