Deep Learning · TEE · section
Learning and the numbers we adjust
Course vocabulary in plain words
| Term | Meaning | Tiny example |
|---|---|---|
| Input / feature | A number given to the model | Image pixel intensity |
| Sample | One example processed by the model | One labeled image |
| Dataset | A collection of examples | 1,000 training images |
| Target / label | Correct answer supplied for training | Class 2 is correct |
| Prediction | Model's current answer | Class probabilities |
| Weight | Learned multiplier on a connection | 0.4 multiplying an input |
| Bias | Learned value added to a weighted sum | z = wx + b |
| Parameter | A value learned during training | Weight or bias |
| Hyperparameter | A setting chosen for training | Learning rate or batch size |
| Activation | Transformation of a neuron's raw score | ReLU or sigmoid |
| Loss | Numerical measure of prediction error | Mean squared error |
| Gradient | Local rate of loss change for parameters | Positive g means locally uphill |
| Optimizer | Rule using gradients to update parameters | Momentum or Adam |
| Epoch | One pass through the training dataset | All 100 samples processed |
| Iteration | One parameter update in these exercises | One mini-batch update |
| Inference | Applying the trained model | Predicting a new image's class |