A technique where a model developed for one task is reused for a different but related task, improving efficiency.

A deep convolutional neural network architecture known for its simplicity and effectiveness in image classification tasks.

A deep residual network architecture that utilizes skip connections, allowing for deeper networks without performance degradation.

A subset of machine learning involving neural networks with multiple layers, enabling the modeling of complex patterns.

The task of assigning a label to an image based on its content, commonly used in computer vision applications.

The process of adjusting a pre-trained model on a new dataset to improve its performance for a specific task.

The optimization of model parameters that are set before training, impacting the model's performance significantly.

Techniques used to artificially expand the size of a training dataset by applying transformations to the existing data.

One complete pass through the entire training dataset during the training process of a model.

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 actual positives, reflecting the model's ability to identify relevant instances.

The harmonic mean of precision and recall, providing a single metric that balances both aspects of model performance.

A systematic approach to applying transfer learning, including selection of base models and adaptation techniques.

A class of deep learning models specifically designed to process structured grid data like images.

A modeling error that occurs when a model learns noise in the training data, performing poorly on unseen data.

A situation where a model is too simple to capture the underlying trend of the data, leading to poor performance.

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

Practical uses of transfer learning across various domains, including healthcare, security, and more.

Models that have been previously trained on large datasets and can be adapted for specific tasks with minimal training.

A collection of images used for training and evaluating machine learning models, often labeled for supervised learning.

A summary of the precision, recall, and F1-score for each class in a classification problem.

Practical implementations of theoretical concepts in industry settings, demonstrating the utility of learned skills.

The setup required for coding and training machine learning models, including libraries and frameworks.

In-depth analyses of specific instances where transfer learning has been successfully applied in real-world scenarios.

Quantitative measures used to assess how well a model performs against a given dataset.