The process of categorizing images into predefined classes based on their content.

A subset of artificial intelligence that enables systems to learn from data and improve over time without explicit programming.

Computational models inspired by the human brain, used to recognize patterns in data.

The techniques applied to raw data to prepare it for analysis, including cleaning and transformation.

A high-level programming language widely used in data science and machine learning for its simplicity and versatility.

A large database of handwritten digits used for training various image processing systems.

The process of scaling data to a standard range, improving model performance.

Techniques used to artificially expand the size of a dataset by creating modified versions of images.

A subset of data used to train a machine learning model, allowing it to learn patterns.

A separate subset of data used to evaluate the performance of a trained model.

A method of evaluating how well a machine learning model performs, guiding its optimization.

One complete pass through the entire training dataset during the training process.

A modeling error that occurs when a model learns noise in the training data instead of the actual pattern.

A scenario where a model is too simple to capture the underlying trend of the data.

The ratio of correctly predicted instances to the total instances in a dataset.

The ratio of true positive predictions to the total predicted positives, measuring the quality of positive predictions.

The ratio of true positive predictions to the total actual positives, indicating the model's ability to find all relevant cases.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

The process of optimizing the parameters that govern the training process of a model.

Techniques used to prevent overfitting by adding a penalty to the loss function.

A mathematical function applied to the output of a neural network layer to introduce non-linearity.

The means by which a user interacts with a machine learning model or application.

Written descriptions of code and processes, essential for understanding and maintaining a project.

The output generated by a machine learning model based on input data.

The process of assessing a model's performance using various metrics to ensure its effectiveness.