[Blog Reads] August 2016


Here we go for another round of blog reads! Here are my picks.

Computer Science

Teach Yourself Programming in Ten Years by Peter Norvig. Availabe in 25 languages, this article is one of the most famous editorial about programming in the world. I love this quote the post gave us: as Alan Perlis once said, “A language that doesn’t affect the way you think about programming, is not worth knowing”. With the trend of learning to program in hours/days, Norvig tells us that this is not the right way to learn a language. It takes time to master anything, may it be piano, drawing, sports, mathematics or programming. The IT field may be moving fast, but to become truly proficient in a programming language, it will take 10 years.

Degrees of separation on a tree algorithm A study on the degree of seperation for an unconventional algorithmic tree. It may look trivial at first, but this is a great way to look at communication problems at large scale.

AngelHack One of the biggest international hackathon in the world, AngelHack recently published two lists. The first one list every team and their projects that won the local AngelHAck held in their city. The second post talks about five teams who won in previous years and are now working on amazing projects. All in all, these posts may give you ideas for your next hackathon participation 😉

Data Science

Machine Vision’s Achilles’ Heel Revealed by Google Brain Researchers A quick introduction to machine vision and its greatest adversarial, the adversarial images.

NYC Subway Math Another cool post from a passionate data researcher as he tries out the New York Subway API to see what kind of data he can find.

Researchers use neural networks to turn face sketches into photos The amazing world of face neural networks keep improving. Today, we can even use sketches and turn them into photos with amazing accuracy.

Data Visualization

What should everyone know about making good charts and graphs to represent data? If anyone wants to venture into data visualization, then they need to read this post! People share their tricks on what is a good vs bad visualization work.

Machine Learning and Artificial Intelligence

AI Revolution 101 This article introduces the reader to the deep world of Artificial Intelligence, while using simple and comprehensible words. We live in a world where what we create in one year may take thousands of years to past civilizations to do the same. Things are moving fast and that is why Artificial Intelligence exist. The text explains what is ANI (Artificial Narrow Intelligence), AGI (Artificial General Intelligence), and ASI (Artificial Super Intelligence). With the mix of evolution and technology, humans are going toward the point of Singularity.

Machine Learning is Fun! Different from another link I shared previously, this is another interesting look at the science of Machine Learning, in a lot more words. It explains the basic notion of supervised Learning in this first post.


Desmos Art: A Definitive Guide to Computational Sketching Are you curious to create your own reddit avator (for a subreddit) using mathematics? This article shows you how to do so using both mathematical and computer science algorithms. Eye opening and with amazing results!

Social Media

Twitter Facial Analysis Reveals Demographics of Presidential Campaign Followers To help keep track of the popularity and voting likelihood at the next U.S. Presidential Elections, a team of researcher are analyzing users’ profile pictures on Twitter to determinate their gender, ethnicity, and social influence.

How does LinkedIn’s recommendation system work? Ever wondered how Linkedin uses Machine Learning? We all know its “professional connections” algorithms is a complex one that uses it data science, but on Quora, people are actually trying to explain the recommendation system… and share their complaints.

How does Airbnb use data science and machine learning? Another great question posted on Quora. Here, a commenter explains the use of ML in three specific fields: image quality (classification based on comfort vs. living area), geographical modeling, and market distribution. A nice read supported by graphs.

Women Empowerment

#iLookLikeAnEngineer: One Year Later Blogger Isis Anchalee goes back in memory land since the first day the hashtag #iLookLikeAnEngineer was used. Through pictures proofs of mostly other women, the hashtag is trying to break the image that engineers are all men.


How the Trump Presidential Campaign is Affecting Trump Businesses An interesting medium post talking about how the Trump Presidential campaign is affecting negatively his business ventures. Supported by data and graphs, the study shows that customers especially during the summer are less inclined to purchase lodging in a Trump hotel or buy his products/services.

Team USA by the Numbers Stats about the age, height, and weight of this year’s American athletes at the Olympics Rio 2016.

Why there hasn’t been found a mathematical model to beat the stock market? A quick discussion on the advancement of data analytics and the use of algorithms to beat the stock market.

Quotable Quote by Marc Levy A great quote that makes you question on how you spend your time.

“Imagine there is a bank account that credits your account each morning with $86,400. It carries over no balance from day to day. Every evening the bank deletes whatever part of the balance you failed to used during the day. What would you do? Draw out every cent, of course? Each of us has such a bank, it’s name is time. Every morning, it credits you 86,400 seconds. Every night it writes off at a lost, whatever of this you failed to invest to a good purpose. It carries over no balance. It allows no over draft. Each day it opens a new account for you. Each night it burns the remains of the day. If you fail to use the day’s deposits, the loss is yours. There is no drawing against “tomorrow”. You must live in the present on today’s deposits. Invest it so as to get from it the utmost in health, happiness, and health. The clock is running. Make the most of today.”
Marc Levy, If Only It Were True


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