Address
7 Bell Yard, London, WC2A 2JR
Work Hours
Monday to Friday: 8AM - 6PM
Model training, reinforcement learning, fine-tuning
Federated learning is a way of training machine learning models without ever moving the raw data. Instead of sending data to a central system, each participating device or organisation trains the model locally and shares only the model updates. The…
Feature engineering is the work of turning raw data into something a machine learning model can learn from. Real-world data is often messy, inconsistent, and full of detail that does not help an algorithm. Feature engineering shapes that data into…
Ensemble Learning is a technique in machine learning that combines multiple models to improve predictive performance, accuracy, and stability. Instead of relying on a single model, ensemble methods merge the strengths of several to reduce bias, variance, and overfitting. The…
Data augmentation is a technique used in machine learning and deep learning to artificially expand the size and diversity of a training dataset. It involves applying transformations, edits, or variations to existing data to create new examples, helping models generalise…
The Cooperative Principle is a theory from linguistics proposed by philosopher H.P. Grice, describing how people naturally cooperate in conversation to achieve effective communication. It suggests that participants typically follow shared rules or “maxims” to make dialogue meaningful, relevant, and…
A context window refers to the amount of text (or tokens) that an AI language model can process and “remember” at one time. It defines how much surrounding information the model considers when generating a response, influencing both accuracy and…
Agentic AI refers to artificial intelligence systems capable of performing tasks proactively with autonomy and purpose, rather than simply responding to direct instructions. These systems use reasoning, planning, and self-directed goals to complete complex workflows without continuous human input. In…