Weekly Article Roundup- July 18-22

Five Things I Learned from Making a Chart out of Body Parts

A hilarious look at the process of data visualization.

Non-Mathematical Feature Engineering Techniques for Data Science

“Apply Machine Learning like the great engineer you are, not like the great Machine Learning expert you aren’t.”
Word.

How to Start Deep Learning

A great list of resources to get started on deep learning!
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Weekly Article Roundup: July 11-15

Why to Not Trust Statistics

I’m in the process of studying for a data science program, and this is a long held belief of mine… one I’ll have to get over. At the very least I can do them some justice?

A Look Inside Facebook’s Data Center

Not an article but more of a slide show… still interesting since they open source their server and network designs.

An Introduction to Model-Based Machine Learning 

To make up for my lack of article on my last post, this is a pretty meaty explanation to model-based ML. Incredibly well-written.

Weekly Article Roundup- July 4- July 8

It’s been a busy week so my comments are abbreviated, but still some great articles published! Definitely check them out.

Study Poses Major Flaw in Classic Artificial Intelligence Test

An interesting look at the classic test.

Every Noise at Once

I’m an audio nerd at heart… I love these visualizations.

 

Surprising History of the Infographic

As soon as we were able to get ahold of more data in the 19th century, we’d turn it into pictures. A great look at the history of visualizing numbers.

Weekly Article Roundup: June 27-July 1

Non-Mathematical Feature Engineering Techniques for Data Science

I like this article because it encourages thinking outside of just making sure that your algorithm is perfect. Both sides of the brain go into representing data in such a way that your message is communicated.

Statistics is Dead: Long Live Data Science

Interesting take on the fact that traditional statistics is mostly used in the realm of data science.

 

Sweet and Short Introduction to Complexity Science

Like the title says 😉

Weekly Article Roundup: June 20-24

The Three Types of Dwayne ‘The Rock’ Johnson Movies

I don’t know if I’ve ever seen any movie with this dude in it, but by all accounts I haven’t seen most movies (Scarface, The Wizard of Oz have seemed to offend people the most). That being said, The Rock seems like a pretty nice guy, and I find it amusing that by not even seeing his movies that this infographic makes sense.

How Google is Remaking Itself as a “Machine Learning First” Company

In order to try and get ahead in the artificial intelligence game, Google is now offering a “machine learning ninja training” program to its engineers.

Weekly Article Roundup- June 13-17

Nearly impossible to predict mass shootings with current data

A tough subject this week but an important set of data to look at, since data relies on trends, not the “one off” cases.

 

Everything you know about AI is wrong

A good article that will but some minds at ease (man-killing robots likely to be much further off than one would think), while AI may be replacing some lower skill level occupations.

 

Big Data is Still Only a Little Helpful

We are still only able to use a small fraction of the data that is generated every day… great read!

Weekly Article Roundup- June 6-10

Commute Map

This is an incredible and somewhat hypnotizing data visualization that shows where people commute to work for any given country. Living in the Bay Area, one of the most stupidly expensive housing markets in the country, it was no surprise to see a wide range of dots all converge on San Jose.

 

Statistical Natural Language Processing in Python

Alternate titles to this post: ‘How to Do Things With Words. And Counters’ or ‘Everything I nEeded to Know About NLP I Learned from Sesame Street. Except Kneser-Ney Smoothing. The Count Didn’t Cover That’

 

Building a Data Science Portfolio

A good list of starting points to build up your portfolio for an interview.

5 Tips for Learning to Code for Visualization

These tips can be broken down for learning anything: Pick an environment you’re comfortable with, learn the basics, pick a project, RTFM (read the f***ing manual, as we say in the audio world), and go and do it!