Deep Learning

Active learning is a powerful technique to improve the efficiency of machine learning algorithms. Learn how it works with practical use cases.

Learn about the latest autoencoder models and how they work. Discover different types of autoencoders and their real-world applications.

Vision Transformers (ViT) brought recent breakthroughs in Computer Vision achieving state-of-the-art accuracy with better efficiency.

Learn about image segmentation with deep learning and the most important datasets. Find the most popular applications of image segmentation.

Learn about self-supervised learning: What it is, examples and methods for computer vision. Fewer labels and smaller samples as new standard?

Easy-to-understand guide about Deep Reinforcement Learning. How it works, examples, and real-world applications of AI based decision-making.

Computer vision systems in multiple fields utilize pose estimation. Pose estimation operates by finding key-points of a person or object and track the pose and orientation.

A complete guide to Natural Language Processing (NLP). How computers can understand text and voice data. Powerful applications and use cases.

Differences, advantages, and practical examples of supervised and unsupervised learning methods – Learn when to use which.