Category: Model Training & Tuning

Self-Labeling in Semi-Supervised Learning

Self-labelling in semi-supervised learning is a method where a machine learning model uses its own predictions to assign labels to unlabelled data. The model is initially trained on a small set of labelled examples and then predicts labels for the unlabelled data. These predicted labels are treated as if they are correct, and the model…

Continual Pretraining Strategies

Continual pretraining strategies refer to methods for keeping machine learning models, especially large language models, up to date by regularly training them on new data. Instead of training a model once and leaving it unchanged, continual pretraining allows the model to adapt to recent information and changing language patterns. This approach helps maintain the model’s…

Neural Network Weight Initialisation Techniques

Neural network weight initialisation techniques are methods used to set the starting values for the weights in a neural network before training begins. These starting values can greatly affect how well and how quickly a network learns. Good initialisation helps prevent problems like vanishing or exploding gradients, which can slow down or stop learning altogether.

Adaptive Learning Rates in Deep Learning

Adaptive learning rates are techniques used in deep learning to automatically adjust how quickly a model learns during training. Instead of keeping the pace of learning constant, these methods change the learning rate based on how the training is progressing. This helps the model learn more efficiently and can prevent problems like getting stuck or…

Structured Prompt Testing Sets

Structured prompt testing sets are organised collections of input prompts and expected outputs used to systematically test and evaluate AI language models. These sets help developers check how well the model responds to different instructions, scenarios, or questions. By using structured sets, it is easier to spot errors, inconsistencies, or biases in the model’s behaviour.

Continuous Prompt Improvement

Continuous Prompt Improvement is the ongoing process of refining and adjusting instructions given to AI systems to achieve better results. By regularly reviewing and updating prompts, users can make sure that the AI understands their requests more clearly and produces more accurate or useful outputs. This process often involves testing different wording, formats, or examples…

Language Domain Classifiers

Language domain classifiers are computer systems or algorithms that automatically identify the subject area or context of a piece of text, such as science, law, medicine, or sports. They work by analysing words, phrases, and writing styles to determine the most likely domain the text belongs to. These classifiers help organise information, improve search, and…

Session-Based Model Switching

Session-Based Model Switching is a method where a software system dynamically changes the underlying machine learning model or algorithm it uses based on the current user session. This allows the system to better adapt to individual user preferences or needs during each session. The approach helps improve relevance and accuracy by selecting the most suitable…

Context-Aware Model Selection

Context-aware model selection is the process of choosing the best machine learning or statistical model by considering the specific circumstances or environment in which the model will be used. Rather than picking a model based only on general performance metrics, it takes into account factors like available data, user needs, computational resources, and the problem’s…