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. …

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 run Caer for the reasons mentioned above. …

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 from dogs (and vice-versa) with sizable accuracy. …

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 on image recognition models, you may consider increasing the diversity of your available dataset by employing data augmentation. These techniques include anything from flipping an image over an axis and adding noise to zooming in on the image. If you are a strong machine learning engineer, you could also try data augmentation with GANs. …

For those in a hurry, here is the course:

For others:

A couple of months back, I wrote a post on how to get into Data Science. I’ve received hundreds of emails since from people all over the world, and the positive (and negative) reviews I got made me realize one thing: for new entrants to the field, Data Science is hard.

And no surprise there, Data Science simple is hard. There’s a lot of Computer Science involved besides the heavy Mathematics that comes along with it. …

Data Science can be overwhelming. There are plenty who’ll tell you that in order to become a data scientist, you’ll have to learn heavy mathematics and computer science — Statistics, Calculus, Linear Algebra, Distributed Computing, Clustering and more.

It’s easy to feel like an antelope caught in the headlights — with no clue where to get started.

I understand what you’re feeling — I’ve been in your shoes not too long ago.

This post is dedicated to the resources that I wish I had at my disposal as a beginner to Data Science. I hope that sharing my experience in this post will put you on the right track to pursuing a data science career and make your learning journey that much more enjoyable. …

Any data scientist or machine learning enthusiast who has been trying to elicit performance of training models at scale will at some point hit a cap and start to experience various degrees of processing lag. Tasks that take minutes with smaller training sets may now take more hours — in some cases weeks — when datasets get larger.

But what are GPUs? How do they stack up against CPUs? Do I need one for my deep learning projects?

If you’ve ever asked yourself these questions, read on…

I recently open-sourced my Computer Vision library that utilizes the GPU for image and video processing on the fly. …