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This workshop is an introduction to Contrastive Learning. We begin by defining what Contrastive Learning is and how it fundamentally differs from traditional supervised learning methods. The session will delve into the core principle of learning by comparing, emphasizing how this method enables models to understand nuances by contrasting similar and dissimilar data points. Key topics include understanding positive and negative pairs in datasets, and the role of loss functions like triplet loss and contrastive loss in model training. We will also explore the wide-ranging applications of Contrastive Learning, from enhancing computer vision systems to improving natural language processing models. The workshop will be designed with illustrative examples and case studies, providing a clear view of how Contrastive Learning is applied in real-world scenarios. This session is ideal for those interested in the cutting-edge methodologies transforming the field of machine learning.