Self-supervised and Unsupervised Learning in Deep Learning

This chapter discusses self-supervised and unsupervised learning approaches in deep learning. It covers clustering-based approaches, dimensionality reduction techniques, and recent advancements in self-supervised learning such as SimCLR, BYOL, and MoCo. Applications in computer vision, natural language processing, and anomaly detection are explored. The chapter provides implementation examples using TensorFlow and PyTorch, along with case studies and advanced topics in scalability and hybrid models.