A deep learning model designed for processing structured grid data, like images, using layers of convolutional filters.

A lightweight web framework for Python that allows for easy development of web applications, including model deployment.

The process of optimizing model parameters that are set before training to improve performance.

Techniques used to increase the diversity of training data by applying transformations like rotation and flipping.

The task of assigning a label to an image based on its content, often using machine learning models.

A subset of data used to train a machine learning model, helping it learn to make predictions.

Data used to tune model parameters and prevent overfitting during training.

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

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

A table used to evaluate the performance of a classification model by comparing predicted and actual labels.

The ratio of true positive predictions to the total predicted positives, indicating the accuracy of positive predictions.

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

The harmonic mean of precision and recall, providing a balance between the two metrics.

A mathematical function applied to a neuron's output, introducing non-linearity into the model.

A layer in a CNN that reduces the spatial dimensions of the input, helping to decrease computation and prevent overfitting.

A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.

The number of training examples utilized in one iteration of model training.

The number of complete passes through the training dataset during the training process.

A regularization technique where randomly selected neurons are ignored during training to prevent overfitting.

A technique where a pre-trained model is fine-tuned on a new task, leveraging previously learned features.

The process of assessing the performance of a trained model using various metrics.

The visual part of a web application that allows users to interact with the model, such as uploading images.

The process of integrating a machine learning model into a production environment for use in real-world applications.

A set of rules that allows different software entities to communicate, often used in web applications for model access.

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

A series of data processing steps that transform raw data into a format suitable for analysis or modeling.