Overview
Semi-supervised learning is a powerful approach in machine learning that combines both labeled and unlabeled data to enhance model performance. By leveraging the strengths of both data types, it allows for more accurate predictions while reducing the reliance on extensive labeled datasets, which can...
Key Terms
Example: Images of cats labeled as 'cat'.
Example: A collection of random images without any labels.
Example: Using a model to predict labels for new images.
Example: One model uses text features while another uses image features.
Example: An accuracy of 90% means 90 out of 100 predictions were correct.
Example: Rotating or flipping images to create new training examples.
Related Topics
Unsupervised Learning
Focuses on learning patterns from unlabeled data without any supervision.
intermediateDeep Learning
A subset of machine learning that uses neural networks with many layers.
advancedTransfer Learning
A technique where a model developed for one task is reused for a different but related task.
intermediateKey Concepts
Labeled DataUnlabeled DataModel TrainingData Augmentation