36What are active learning and semi-supervised learning, and when do you use them?▼mediumGoogleAmazonMeta1 replies◆ premiumLabels are the costly bottleneck in ML, and these two techniques tackle it from different angles. What matters is knowing active learning picks what to label while semi-supervised draws on unlabeled data directly. Here is the answer.Open full answer →
62What is weak supervision, and how do you train models with noisy or programmatic labels?▼mediumGoogleAmazonSnorkel2 replies◆ premiumHand-labeling at scale is the bottleneck. Weak supervision produces labels programmatically instead, and what matters is whether you can explain how a label model denoises conflicting sources into probabilistic labels. Here is the answer.Open full answer →
74What is positive-unlabeled (PU) learning, and when do you need it?▼hardAmazonGoogleMeta1 replies◆ premiumPlenty of real problems hand you confirmed positives but never confirmed negatives, only unlabeled data. The shortcut everyone grabs quietly biases the model. What shows depth is naming the regime and its fix. Here is the answer.Open full answer →
