| Label |
The data that we’re trying to predict, such as “dog” or “cat” |
| Architecture |
The template of the model that we’re trying to fit; the actual mathematical function that we’re passing the input data and parameters to |
| Model |
The combination of the architecture with a particular set of parameters |
| Parameters |
The values in the model that change what task it can do, and are updated through model training |
| Fit |
Update the parameters of the model such that the predictions of the model using the input data match the target labels |
| Train |
A synonym for fit |
| Pretrained model |
A model that has already been trained, generally using a large dataset, and will be fine-tuned |
| Fine-tune |
Update a pretrained model for a different task |
| Epoch |
One complete pass through the input data |
| Loss |
A measure of how good the model is, chosen to drive training via SGD |
| Metric |
A measurement of how good the model is, using the validation set, chosen for human consumption |
| Validation set |
A set of data held out from training, used only for measuring how good the model is |
| Training set |
The data used for fitting the model; does not include any data from the validation set |
| Overfitting |
Training a model in such a way that it remembers specific features of the input data, rather than generalizing well to data not seen during training |
| CNN |
Convolutional neural network; a type of neural network that works particularly well for computer vision tasks |
