Posts

In this article, I will be discussing the types of generic tests, their benefits, and the important differences between them

This post will talk about the best practices for achieving high code coverage in frontend applications.

If you’re a self-taught programmer, you’re probably familiar with the struggle of deciding which direction to upskill in. After all, trends in the job market fluctuate, making it challenging to pin down what skills employers will want even after a few months spent learning that new skill.

Sometimes there are scenarios where you have way lesser number of samples for some of the classes where as other classes have lots of samples. In such a scenario, you don’t want a training batch to be contain samples just from a few of the classes with lots of samples.

Recently, while working on a video dataset, I noticed that some of the videos contained a few corrupted frames. While running the training, my dataloader used to return an incorrect shaped tensor since it was not able to read the corrupted frame.

Recently, I worked on an assignment to analyze the data from bikesharing system to predict its demand. In this post, we will see how the given data can be analyzed using statistical machine learning methods.

In this post we will talk about generating synthetic data from tabular data using Generative adversarial networks(GANs). We will be using the default implementation of CTGAN [1] model. Introduction In the last post on GANs we saw how to generate synthetic data on Synthea dataset.