Unsupervised Learning
Unsupervised Learning in Convolutional Neural Networks focuses on discovering patterns and structures within data without explicit labels. It enables CNNs to learn representations of images, videos, and other high-dimensional data by identifying intrinsic properties and dependencies, resulting in robust features for clustering, dimensionality reduction, density estimation, and anomaly detection tasks. By unlocking the potential of unsupervised techniques like autoencoders, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and clustering algorithms, we can significantly enhance CNN's performance in various applications across Technology, Artificial Intelligence, and Deep Learning.