[Blog Reads] June 2017

With the addition of this post, I have officially written a whole year of “Blog Reads”. The first post was published on July 23rd 2016. Now, we are in July 2017. Time sure passes fast! I am glad I decided to go through this experiment that encouraged me to continuously write on the blog, even during off time. It made me go read on various blogs, Medium, WordPress, Quora, etc. I learned a lot about Data Science, Machine Learning as well as other fields. From now on, I will change the format of this segment by a little bit.

Starting from July 2017, I will regularly post a summary/review of one specific research paper or blog post that I found meaningful and interesting to cover. By changing the format, I am hoping that this will encourage myself to really understand the theory and applications of the research in depth. For today however, let’s go through one last list of most interesting blog posts I found for the month of June!

Interesting Projects

  • Clustering the New York City of the 21st Century into 5 Boroughs naturally (link)
  • How Data Science Helps Power Worldwide Delivery of Netflix Content (link)
  • If Taxi Trips were Fireflies: 1.3 Billion NYC Taxi Trips Plotted (link)
  • Space, Time and Groceries: Grocery delivery visualized in python with datashader (link)
  • Recommendation Systems at Amazon (link)
  • Watch 35 Years of the World’s Economy Evolving as a Living Organism (link) (video link)
  • Data-Mining 100 Million Instagram Photos Reveals Global Clothing Patterns (link)
  • Using AI to predict Suicide (link)
  • A Day in the Life of Americans (link)
  • Building Dot Density Maps with UK Census Data in R (link)
  • Work in progress: Portraits of Imaginary People (link)

AI Research

  • An Algorithm Summarizes Lengthy Text Surprisingly Well (link)
  • NVIDIA’s Self-Driving Car AI (link)
  • A new algorithm for finding a visual center of a polygon (link)


Google launches its AI-powered jobs search engine Google recently launched an Application on Android that allows you to search for jobs on multiple job boards and company website online. It then uses ML to categorize the job postings in easy to use format. Google’s goal with this app is to make job searching easier.

How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native If you are like me, you are totally in love with HBO’s Silicon Valley. An awesome comedy TV series with amazing cast. The team behind the show recently released an article explaining how one of the show’s featured app “Not Hotdog” made it to the app store..


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