What is Semi-Supervised Learning?

Summary: The video discusses the process of building an AI model for recognizing pictures of cats and dogs, highlighting the challenges of supervised learning and how semi-supervised learning can address these issues by effectively utilizing both labeled and unlabeled data.

Keypoints:

Supervised learning requires a labeled dataset, which can be time-consuming and costly to create.

Semi-supervised learning addresses the labeling challenge by combining a small amount of labeled data with a larger set of unlabeled data.

This approach helps prevent overfitting, where the model learns patterns too specific to the training dataset.

Semi-supervised learning employs various techniques such as:

• Wrapper methods to generate confident pseudo labels for unlabeled data.
• Clustering to group similar data points together.
• Unsupervised pre-processing to extract meaningful features from unlabeled data.
• Active Learning, where humans label only the most uncertain cases.

Combining labeled and unlabeled data through semi-supervised learning results in better models with reduced manual labeling efforts.

The content is provided by the YouTube Channel: IBM Technology.