When Harvard Business Review came out with its article labelling Data Science the sexiest job of the 21st century, it grabbed a lot of eyeballs. Safe to say that that ~3000-word article initiates a mad frenzy as people scrambled to learn Data Science with the eventual hope of becoming a Data Scientist.

About 10 years later, that hype doesn’t seem to be dying anytime soon (if any, it has exploded exponentially).

But before you think about transitioning into this — admittedly — very lucrative field, there are a few truths that no one seems to be talking about, and which…

A week back, I wrote an article on How to Get into Data Science in 2021 and since then I’ve received quite a few emails asking me just how much math is required in Artificial Intelligence.

I won’t lie: It’s a lot of math.

And this is one of the reasons that puts off many beginners. After much research and talks with several veterans in the field, I’ve compiled this no-nonsense guide that covers all of the fundamentals of the math you’ll need to know. …

When I released Caer back in August of this year, I have received hundreds of emails from researchers and computer vision enthusiasts around the world thanking me for releasing the library. Their good (and bad) feedback pushed and motivated me to take the library to another level.

Today, I’m excited to announce the first-ever stable release of Caer, a lightweight open-source Python library that simplifies the way you approach Computer Vision. It abstracts away unnecessary boilerplate code enabling maximum flexibility. …

Opinion

Ever since Python was released in the early 1990s, it has generated a lot of hype. Sure, it took the programming community at least 2 decades to appreciate its existence, but since then, it has far surpassed C, C#, Java and even Javascript in popularity.

Although Python dominates the fields of Data Science and Machine Learning, and, to some extent, Scientific and Mathematical computing, it does have its share of disadvantages when compared to newer languages like Julia, Swift and Java.

What made Python so popular?

One of the main driving points behind Python’s meteoric growth was how easy it was to learn and how…

A couple of weeks ago, I was just about ready to release Caer, a Computer Vision library in Python to be publically available on PyPi, when I decided to send it to a friend in Alberta to tink around with it.

A few days later, I find that he’s still figuring out how to get it to work on his machine.

After dozens of hours, I finally found out why — Caer implemented code from the previous versions of other Python packages that simply weren’t available in their newer releases.

Despite having those packages installed, my friend wasn’t able to…

I’m a Data Science enthusiast and one of the main things I deal with is Data. A lot of it.

With more than 2.5 exabytes of data generated every day, it comes as no surprise that this data needs to be stored somewhere and accessed when required.

This article presents a hackable cheatsheet to get you up and running with SQL quickly!

What is SQL?

SQL stands for Structured Query Language. It is a language for relational database management systems. SQL is used today to store, retrieve and manipulate data within relational databases.

Here’s what a basic relational database looks like.

Almost everything around you today is empowered by Machine Learning and Deep Learning. From the way Google Photos recognizes your face from a bunch of people in an image, to autonomous vehicles, these algorithms have changed the landscape of machine-centred thinking.

Among the most recent (and exciting) contributions is in the field of Computer Vision where insights are derived from media files (images and video). The most noted part is Image Classification — the ability for a machine to distinguish, or classify, between objects when given an image.

In this article, we’ll be building an image classifier to distinguish cats…

So, you have gathered a dataset, built a neural network, and trained your model.

But despite the hours (and sometimes days) of work you invested to create the model, it spits out predictions with an accuracy of 50–70%. Chances are, this is not what you expected.

Here are a few strategies, or hacks, to boost your model’s performance metrics.

1. Get More Data

Deep learning models are only as powerful as the data you bring in. One of the easiest ways to increase validation accuracy is to add more data. This is especially useful if you don’t have many training instances.

If you’re working…