Self-supervised learning is a type of machine learning where a system teaches itself by finding patterns in unlabelled data. Instead of relying on humans to label the data, the system creates its own tasks and learns from them. This approach allows computers to make use of large amounts of raw data, which are often easier…

Catastrophic forgetting is a problem in machine learning where a model trained on new data quickly loses its ability to recall or perform well on tasks it previously learned. This happens most often when a neural network is trained on one task, then retrained on a different task without access to the original data. As…

Curriculum Learning is a method in machine learning where a model is trained on easier examples first, then gradually introduced to more difficult ones. This approach is inspired by how humans often learn, starting with basic concepts before moving on to more complex ideas. The goal is to help the model learn more effectively and…