Contrastive learning is a type of unsupervised learning technique used in deep learning models. The primary goal of contrastive learning is to learn useful representations of data by comparing different examples in a dataset. This comparison is used to learn similarities and differences between examples and create a feature space where similar examples are clustered together. Contrastive learning has been widely used in various fields, such as computer vision, natural language processing, and speech recognition, to learn robust and discriminative features for downstream tasks. In this blog post, we will provide an overview of contrastive learning, including its loss type, intuition, and implementation using Keras or PyTorch libraries for image classification. Intuition The intuition behind contrastive learning is straightforward. Given two examples, the model learns to compare their features and assign similar examples to the same point in the feature space and dissimilar examples to