ML

What is image classification and how does it work in machine learning? Let's explore the algorithms and deep neural networks for image classification.

Learn about convolutional neural networks and their development from the early 90s: a full timeline, application rundown, and much more.

As diverse as OCR is, it can start with the simplest action like scanning a text to learn about its origin or translating it. Read our blog for more.

Get a feel of how neural networks work on LiDAR data, how LiDAR differs from 2D images, and how LiDAR annotation works.

Wondering which dataset to use to get started with ML model training? Check out our comprehensive blog post on the COCO dataset.

Get to know the differences between overfitting and underfitting in machine learning, learn how to detect and prevent them, and much more.

Here is all you need to know about image annotation, its types, and techniques to take your annotation process to the next level.

This article covers the essentials of gradient descent, how it’s applied in real life, and how to improve your gradient descent algorithm.

What is Human-Centered AI? How much can we get involved in its development and its ability to enhance our lives? This article will cover it all.

What do we do about biases in machine learning? Explore the types of ML/AI bias, measurement methods, and mitigation strategies in this article.

Do you think it’s time to optimize your ML pipeline? Check out the staples before getting started.

Don’t want to let your data lake turn into a swamp? Check out this article to learn the differences, applications, and more.