Red Teaming
Red Teaming is deliberately stress-testing an AI system to find harmful or unsafe behaviours before release.
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Computer Vision
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Deep Learning
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Fundamentals
Generative AI
Infrastructure
Machine Learning
NLP
Hallucination
Hallucination is when an AI model produces confident but false or fabricated information.
Catastrophic Forgetting
Catastrophic Forgetting is when a model trained on new data loses what it previously learned.
Overfitting
Overfitting is when a model learns the training data too closely, including its noise, and performs…
Active Learning
Active Learning is a strategy where the model chooses the most useful examples for humans to…
Underfitting
Underfitting is when a model is too simple to capture the underlying pattern in the data…
Self-Supervised Learning
Self-Supervised Learning is learning from unlabelled data by creating its own training signals, key to modern…
Model
Model is the trained mathematical system that takes input data and produces predictions or generated output.
Training Data
Training Data is the dataset used to teach a machine learning model the patterns it needs…
Feature
Feature is an individual measurable property of the data that a model uses to make predictions.
Feature Engineering
Feature Engineering is the process of selecting and transforming raw data into features that improve model…
Parameter
Parameter is an internal value a model learns during training; large models have billions of them.
Hyperparameter
Hyperparameter is a configuration set before training — like learning rate or batch size — that…
Gradient Descent
Gradient Descent is an optimisation method that iteratively adjusts model parameters to reduce prediction error.
Backpropagation
Backpropagation is the algorithm that calculates how to adjust each parameter in a neural network to…
Loss Function
Loss Function is a measure of how far a model's predictions are from the correct answers,…
Epoch
Epoch is one full pass of the training algorithm over the entire training dataset.
Batch Size
Batch Size is the number of training examples processed before the model updates its parameters.
Learning Rate
Learning Rate is a setting that controls how big each step is when a model updates…
Inference
Inference is the stage where a trained model is used to make predictions or generate output…
Latency
Latency is the time it takes for an AI system to return a result after receiving…
GPU
GPU is a graphics processing unit, hardware widely used to train and run AI models because…