AI Methods & Techniques

Reinforcement learning is a type of machine learning where a system learns by trying different actions and getting feedback. Think of it like training a dog. The dog tries different behaviours, gets treats for good actions, and learns what works…

Embeddings turn text, images, and other content into arrays of numbers that capture meaning. Think of them as coordinates in a mathematical space where similar content clusters together. A sentence about databases sits near other database related content. A product…

Synthetic data is artificial information generated by algorithms to mimic real data without containing any actual records from real people or events. Think of it as a statistical twin: it shares the patterns, correlations, and structure of your original dataset,…

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models by connecting them to live data sources.

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…

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…

Fine-tuning is the process of taking a pre-trained machine learning or large language model (LLM) and training it further on a smaller, domain-specific dataset. The goal is to adapt the model’s general knowledge to perform better on tasks relevant to…

Generative Adversarial Networks (GANs) are a class of machine learning models used to generate new, realistic data by training two neural networks in competition with one another. This adversarial setup enables AI systems to create synthetic data, such as images,…

A hyperparameter is a configuration setting used to control the learning process of a machine learning model. Unlike model parameters, which are learned automatically during training, hyperparameters are defined manually before the training begins and directly influence how efficiently and…

Model validation and testing are the processes used to evaluate how accurately and reliably an artificial intelligence or machine learning model performs before it’s deployed in production. They ensure that models make trustworthy predictions, generalise well to new data, and…