https://coursiv.io/blog/tags/foundationsRecent content in Foundations on Coursiv BlogHugo -- 0.147.0en-UShttps://coursiv.io/blog/glossary/activation-functionMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/activation-functionThe non-linear step applied at each unit, without which a deep network would collapse into a single linear one.https://coursiv.io/blog/glossary/artificial-general-intelligenceMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/artificial-general-intelligenceA hypothetical system matching human breadth across essentially any cognitive task — no agreed definition, no agreed timeline.https://coursiv.io/blog/glossary/artificial-intelligenceMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/artificial-intelligenceSoftware that performs tasks we normally associate with human thinking — recognising, predicting, generating, deciding.https://coursiv.io/blog/glossary/backpropagationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/backpropagationThe algorithm that works out how much each weight contributed to the error, so gradient descent can fix it.https://coursiv.io/blog/glossary/batch-sizeMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/batch-sizeHow many examples the model processes before updating its weights once.https://coursiv.io/blog/glossary/classificationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/classificationPredicting which category something belongs to.https://coursiv.io/blog/glossary/deep-learningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/deep-learningMachine learning built on neural networks with many layers — the approach behind almost every modern AI system.https://coursiv.io/blog/glossary/epochMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/epochOne full pass of the training algorithm over the entire training dataset.https://coursiv.io/blog/glossary/featureMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/featureOne input variable a model uses to make its prediction.https://coursiv.io/blog/glossary/gradient-descentMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/gradient-descentThe optimisation method that finds better parameters by repeatedly stepping downhill on the loss.https://coursiv.io/blog/glossary/hyperparameterMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/hyperparameterA setting you choose before training, as opposed to a parameter the model learns during it.https://coursiv.io/blog/glossary/inferenceMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/inferenceRunning a trained model to get an answer — everything that happens after training is done.https://coursiv.io/blog/glossary/labelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/labelThe correct answer attached to a training example.https://coursiv.io/blog/glossary/learning-rateMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/learning-rateHow big a step training takes each update — the hyperparameter most likely to ruin a run.https://coursiv.io/blog/glossary/loss-functionMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/loss-functionThe formula that scores how wrong a model's prediction is — the thing training tries to minimise.https://coursiv.io/blog/glossary/machine-learningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/machine-learningA way of building software where the system learns patterns from examples instead of following hand-written rules.https://coursiv.io/blog/glossary/modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/modelThe trained artefact — the learned parameters plus the architecture — that turns an input into an output.https://coursiv.io/blog/glossary/narrow-aiMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/narrow-aiAI built for a specific task, which is every AI system that currently exists.https://coursiv.io/blog/glossary/neural-networkMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/neural-networkA model made of layers of connected units that pass numbers forward, loosely inspired by neurons.https://coursiv.io/blog/glossary/overfittingMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/overfittingWhen a model memorises its training data instead of learning the pattern, and fails on anything new.https://coursiv.io/blog/glossary/parameterMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/parameterOne of the internal numbers a model adjusts during training; parameter count is a rough proxy for model capacity.https://coursiv.io/blog/glossary/precision-and-recallMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/precision-and-recallThe two metrics that actually tell you whether a classifier works when one class is rare.https://coursiv.io/blog/glossary/regressionMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/regressionPredicting a number rather than a category.https://coursiv.io/blog/glossary/regularizationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/regularizationAny technique that deliberately constrains a model to stop it memorising the training set.https://coursiv.io/blog/glossary/reinforcement-learningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/reinforcement-learningLearning by trial and error, guided by rewards rather than labelled answers.https://coursiv.io/blog/glossary/self-supervised-learningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/self-supervised-learningTraining where the data generates its own labels — the trick that made large language models possible.https://coursiv.io/blog/glossary/supervised-learningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/supervised-learningLearning from examples that come with the right answer attached.https://coursiv.io/blog/glossary/test-dataMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/test-dataData the model has never seen, used once at the end to estimate real-world performance.https://coursiv.io/blog/glossary/training-dataMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/training-dataThe examples a model learns from — the single biggest determinant of what it can and cannot do.https://coursiv.io/blog/glossary/underfittingMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/underfittingWhen a model is too simple to capture the pattern, and is wrong on training and new data alike.https://coursiv.io/blog/glossary/unsupervised-learningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/unsupervised-learningFinding structure in data that has no labels — grouping, compressing, spotting outliers.https://coursiv.io/blog/glossary/validation-dataMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/validation-dataA held-out slice of data used to tune settings and check progress while training, without touching the test set.https://coursiv.io/blog/glossary/weightsMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/weightsThe learned strengths of connections in a neural network — the substance of what a model knows.