Machine learning

Pattern recognition is an integral part of image processing. In this article, we’ll walk you through the staples of pattern recognition in machine learning.

Learn all about supervised learning and other machine learning tasks with this comprehensive guide, and gain insight into how ML models operate.

The best data labeling tools at your fingertips. Check out our detailed overview of the top 30 data labeling solutions in 2023 according to G2.

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.

Everything you need to know about data labeling. This comprehensive guide is all you need to get started.

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

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

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.

Get to know what mAP is, how it is calculated, and why it can’t be used directly. We’ve got all that and beyond covered in this comprehensive post.

Why use an activation function and how to choose the right one to train a neural network? Get answers to these questions and more in this post.

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