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…
Contrastive learning is a machine learning technique that teaches models to recognise similarities and differences between pairs or groups of data. It does this by pulling similar items closer together in a feature space and pushing dissimilar items further apart. This approach helps the model learn more useful and meaningful representations of data, even when…
Latent space refers to a mathematical space where complex data like images, sounds, or texts are represented as simpler numerical values. These values capture the essential features or patterns of the data, making it easier for computers to process and analyse. In machine learning, models often use latent space to find similarities, generate new examples,…
Few-shot prompting is a technique used with large language models where a small number of examples are provided in the prompt to guide the model in performing a specific task. By showing the model a handful of input-output pairs, it can better understand what is expected and generate more accurate responses. This approach is useful…
Multimodal models are artificial intelligence systems designed to understand and process more than one type of data, such as text, images, audio, or video, at the same time. These models combine information from various sources to provide a more complete understanding of complex inputs. By integrating different data types, multimodal models can perform tasks that…
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…
Knowledge distillation is a machine learning technique where a large, complex model teaches a smaller, simpler model to perform the same task. The large model, called the teacher, passes its knowledge to the smaller student model by providing guidance during training. This helps the student model achieve nearly the same performance as the teacher but…