Interesting AI/ML Articles I Came Across This Week

Interesting AI/ML Related Articles I Came Across This Week (Apr 11)

With most of us having more time on our hands to spare, the highlighted articles can prove useful for those looking to learn new things or those merely seeking distracting content

I’ve spent more time on Medium this week in comparison to any other week in 2020, and the same could be said for a large number of readers.

Many of us are currently in lockdown and are looking for ways to pass the time. Some of us are in search of credible information and advice in regards to the ongoing pandemic, while most of us would rather be distracted.

Below are some articles I came across on Medium and have met the criteria that I mentioned above. I would recommend finding one or two articles presented in this article that sit well with you and exploring their contents.

The selected articles are well written, structured, and contain interesting content for different types of machine learning enthusiasts.

Enjoy.

14 Data Science Projects To Do During Your 14 Day Quarantine by Terence Shin

With the imposed lockdown in major countries and cities around the globe, this period is the perfect opportunity for Data Scientists and Machine learning practitioners to improve on existing skills and work on portfolios.

This is why Terence Shin’s article is one that is rightly timed. Terence has presented readers with 14 projects that they can tackle during this uncertain period.

Terence starts his article by expressing gratitude towards the thousands of medical professionals across the globe currently battling the coronavirus pandemic.

The projects within the article are grouped into three sections, namely: visualization, exploratory data analysis, and prediction modeling. The difficulty level of each project is also noted. Beginners can work through at least seven projects labeled as ‘’Easy’’.

The variation of subject matters the projects within the articles addresses are very relevant, be prepared to explore coronavirus and global climate based datasets.

I’ll highly recommend this particular article to individuals starting within the Data Science field or anyone that simply would like to add some more projects to their portfolios.

Coronavirus: Where We Stand and What We Should Do Next by Lex Fridman

Lex Fridman is an individual many machine learning practitioners might already be familiar with.

He recently wrote an article that provides vital statistical information on the coronavirus pandemic. The statistical data within the written piece is centered mainly around the United States and includes information on the economic impact the pandemic as had on the US. It also highlights aspects of difficulties that medical institutions in the US are facing.

In his latest article, Lex provides invaluable information and advice on how we, as individuals, can play a part in helping humanity as a whole get through this pandemic. Reading this article, it’s obvious that we all have a role to play, and everyone is indirectly responsible for the health and safety of the individuals around them.

There are tons of credible resources accompanied by the information provided in the article. The resources provide readers with in-depth details on questions surrounding the constant use of face masks and their effectiveness. He even includes videos that give more information on mask utilization and effectiveness.

I highly recommend this article to be read and shared; the knowledge provided could keep you and your family safe in these unprecedented times.

Top 20 Movies About Artificial Intelligence And Big Data by Benedict Neo

A lot of Medium writers have realized that people now have more time to spare during the lockdown.

Benedict Neo is one of those writers, as he has written an intriguing article that lists movies that are geared towards Artificial intelligence and Data Science.

Going through the list myself, I have already watched 90% of movies listed, and I appreciate the fact that The Matrix was included ( extra claps! ).

Classics such as Star Wars, Robocop, and Terminator are included, along with more modern masterpieces such as Chappie, Interstellar, and Her.

For each presented movie, Benedict writes a summary of what the movie entails, without any spoilers. Also, Included in each summary is a statement of what aspect of Artificial Intelligence and Data Science each movie explores; some are based on the societal impact of AI and other focus technological implications.

I was a bit shocked that Altered Carbon wasn’t included in the list. Nevertheless, this was a good read.

Facebook Open Sources Architecture For Personalized Neural Recommendation Systems by Jesus Rodriguez

This article is geared towards the more technical reader.

Jesus Rodriguez has provided an in-depth summary and evaluation of Facebook’s Deep Neural Network (DNN) architecture for personalized recommendation systems.

The article touches on the point that when it comes to the implementation of personalized recommendation systems, there isn’t a standardized approach that can be utilized.

Jesus mentions that Facebook release of their DNN architectures for recommendation systems: RMC1, RMC2, and RMC3 can be suitable configurable blueprints for anyone embarking on the implementation of recommendation systems.

I find this particular article insightful as it includes visual illustrations that highlight differences between the three of Facebook’s DNN topical architectures and provides on a textual explanation of the critical differences.