32What is transfer learning, and how do you decide whether to freeze, fine-tune, or use feature extraction?▼mediumGoogleAmazonMeta1 replies◆ premiumTransfer learning is the reason you seldom train from scratch, and the real question is how much of the pretrained model to reuse versus adapt. What matters is a clean decision grid across data size and task similarity, plus knowing when a low LR guards against catastrophic forgetting.Open full answer →
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 →
