This segment introduces data augmentation, a technique to artificially increase the size of a dataset by applying transformations (rotation, zooming, adding noise, blurring) to existing data. It explains how these transformations change the input data seen by the neural network without altering the meaning for humans, preventing memorization and improving generalization. The segment also discusses which transformations are more effective and how to apply them during training to prevent overfitting on small datasets. Deep learning needs massive datasets. For small datasets, use transfer learning (pre-train on a related, larger dataset, then fine-tune on your data) and data augmentation (transform images—rotate, zoom—to create variations without changing meaning). These techniques improve model generalization, preventing overfitting. This segment explains the core concept of transfer learning, where a pre-trained neural network on a related, larger dataset is reused and fine-tuned with a new output layer for a smaller, specific problem. It details the process of adding a new output layer, training its weights using the smaller dataset, and the rationale behind expecting this method to work by leveraging shared functionality between related problems. The explanation includes analogies to hand-solving problems and the transformation of input data.
