32What is transfer learning, and how do you decide whether to freeze, fine-tune, or use feature extraction?▼mediumGoogleAmazonMeta1 replies◆ premiumTransfer learning is why you rarely train from scratch, and the real question is how much of the pretrained model to reuse versus adapt. The signal is a crisp decision grid over data size and task similarity, plus knowing when a low LR saves you from catastrophic forgetting.Open full answer →
36What are active learning and semi-supervised learning, and when do you use them?▼mediumGoogleAmazonMeta1 replies◆ premiumLabels are the expensive bottleneck in ML, and these two techniques attack it from different angles. The signal is knowing active learning chooses what to label while semi-supervised uses unlabeled data directly. Here is the answer.Open full answer →
