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…

Neural Architecture Search (NAS) is a process that uses algorithms to automatically design the structure of neural networks. Instead of relying on human experts to decide how many layers or what types of connections a neural network should have, NAS explores many possible designs to find the most effective one for a specific task. This…

Transfer learning is a method in machine learning where a model developed for one task is reused as the starting point for a model on a different but related task. This approach saves time and resources, as it allows knowledge gained from solving one problem to help solve another. It is especially useful when there…

Zero-Shot Learning is a method in machine learning where a model can correctly recognise or classify objects, actions, or data it has never seen before. Instead of relying only on examples from training data, the model uses descriptions or relationships to generalise to new categories. This approach is useful when it is impossible or expensive…