Representation Learning is a type of machine learning that focuses on automatically discovering the best way to represent data. Instead of relying on manual feature extraction, it allows algorithms to learn useful features directly from raw data, such as images or text. This process helps improve the performance of various tasks, like classification or clustering. One common approach in representation learning is using neural networks, particularly deep learning models. These models can capture complex patterns and relationships in data, making them effective for applications in fields like computer vision and natural language processing. By learning representations, these models can generalize better to new, unseen data.