Continual learning is a method in artificial intelligence where systems are designed to keep learning and updating their knowledge over time, instead of only learning once from a fixed set of data. This approach helps machines adapt to new information or tasks without forgetting what they have already learned. It aims to make AI more…
Dynamic Neural Networks are artificial intelligence models that can change their structure or operation as they process data. Unlike traditional neural networks, which have a fixed sequence of layers and operations, dynamic neural networks can adapt in real time based on the input or the task at hand. This flexibility allows them to handle a…
Knowledge amalgamation is the process of combining information, insights, or expertise from different sources to create a more complete understanding of a subject. This approach helps address gaps or inconsistencies in individual pieces of knowledge by bringing them together into a unified whole. It is often used in fields where information is spread across multiple…
Pruning-aware training is a machine learning technique where a model is trained with the knowledge that parts of it will be removed, or pruned, later. This helps the model maintain good performance even after some connections or neurons are taken out to make it smaller or faster. By planning for pruning during training, the final…
Data augmentation strategies are techniques used to increase the amount and variety of data available for training machine learning models. These methods involve creating new, slightly altered versions of existing data, such as flipping, rotating, cropping, or changing the colours in images. The goal is to help models learn better by exposing them to more…
Teacher-Student Models are a technique in machine learning where a larger, more powerful model (the teacher) is used to train a smaller, simpler model (the student). The teacher model first learns a task using lots of data and computational resources. Then, the student model learns by imitating the teacher, allowing it to achieve similar performance…
Knowledge-augmented models are artificial intelligence systems that combine their own trained abilities with external sources of information, such as databases, documents or online resources. This approach helps the models provide more accurate, up-to-date and contextually relevant answers, especially when the information is too vast or changes frequently. By connecting to reliable knowledge sources, these models…
AutoML, short for Automated Machine Learning, refers to tools and techniques that automate parts of the machine learning process. It helps users build, train, and tune machine learning models without requiring deep expertise in coding or data science. AutoML systems can handle tasks like selecting the best algorithms, optimising parameters, and evaluating model performance. This…
Ensemble learning is a technique in machine learning where multiple models, often called learners, are combined to solve a problem and improve performance. Instead of relying on a single model, the predictions from several models are merged to get a more accurate and reliable result. This approach helps to reduce errors and increase the robustness…
Gradient Boosting Machines are a type of machine learning model that combines many simple decision trees to create a more accurate and powerful prediction system. Each tree tries to correct the mistakes made by the previous ones, gradually improving the model’s performance. This method is widely used for tasks like predicting numbers or sorting items…