Dynamic model pruning is a technique used in machine learning to make models faster and more efficient by removing unnecessary parts while the model is running, rather than before or after training. This method allows the model to adapt in real time to different tasks or resource limitations, choosing which parts to use or skip…
Adaptive learning rates are techniques used in training machine learning models where the rate at which the model learns changes automatically during the training process. Instead of using a fixed learning rate, the algorithm adjusts the rate depending on how well the model is improving. This helps the model learn more efficiently, making faster progress…
Neural pattern recognition is a technique where artificial neural networks are trained to identify patterns in data, such as images, sounds or sequences. This process involves feeding large amounts of data to the network, which then learns to recognise specific features and make predictions or classifications based on what it has seen before. It is…
Knowledge fusion models are systems or algorithms that combine information from multiple sources to create a single, more accurate or comprehensive dataset. These models help resolve conflicts, fill in gaps, and reduce errors by evaluating the reliability of different inputs. They are commonly used when data comes from varied origins and may be inconsistent or…
Generalisation optimisation is the process of improving how well a model or system can apply what it has learned to new, unseen situations, rather than just memorising specific examples. It focuses on creating solutions that work broadly, not just for the exact cases they were trained on. This is important in fields like machine learning,…
Domain-specific model tuning is the process of adjusting a machine learning or AI model to perform better on tasks within a particular area or industry. Instead of using a general-purpose model, the model is refined using data and examples from a specific field, such as medicine, law, or finance. This targeted tuning helps the model…
Contrastive learning optimisation is a technique in machine learning where a model learns to tell apart similar and dissimilar items by comparing them in pairs or groups. The goal is to bring similar items closer together in the modelnulls understanding while pushing dissimilar items further apart. This approach helps the model create more useful and…
Model optimisation frameworks are software tools or libraries that help improve the efficiency, speed, and resource use of machine learning models. They provide methods to simplify or compress models, making them faster to run and easier to deploy, especially on devices with limited computing power. These frameworks often automate tasks like reducing model size, converting…
Bayesian Model Optimization is a method for finding the best settings or parameters for a machine learning model by using probability to guide the search. Rather than testing every possible combination, it builds a model of which settings are likely to work well based on previous results. This approach helps to efficiently discover the most…
Feature correlation analysis is a technique used to measure how strongly two or more variables relate to each other within a dataset. This helps to identify which features move together, which can be helpful when building predictive models. By understanding these relationships, one can avoid including redundant information or spot patterns that might be important…