Students who complete this unit will demonstrate that they can:
Embeddings
Students who complete this unit will demonstrate that they can:
Define the following terms: sample, target, loss, batch, epoch, activation function, layer, overfitting
Contrast binary, multiclass, and multilabel classification
Describe the difference between a metric and a loss function.
Explain how a pre-trained model can be repurposed for a new task by separating it into a general-purpose feature extractor (sometimes called a "body" or "encoder") and a task-specific linear classifier (sometimes called "head").
Interpret a linear classifier head as computing similarities in a vector space of embedded data items.