Deep Learning · MT2 · section
Preparation sessions before 13 October
MT2 preparation and examination plan
| Session | What to cover | What to write on paper |
|---|---|---|
| 1 | Essential prerequisite revision only | Derivative of the stated loss; meaning of gradient and learning rate; neuron/CNN notation |
| 2 | GD, batch/SGD/mini-batch, momentum and Nesterov | Two iterations, update counts, old versus new state, lookahead gradient |
| 3 | AdaGrad, RMSProp and Adam | Accumulators, bias correction, sign changes and complete two-step working |
| 4 | Regularization and generalization | L1/L2, dropout, early stopping, underfitting/overfitting, training/validation/test; theory and numerical examples |
| 5 | Convolution, stride, padding and pooling | Every window product/sum, multi-channel bias, floor and SAME-padding cases, max/min/average pooling |
| 6 | CNN shapes, parameters and architectures | Full layer table, flatten/dense count, bottlenecks, residual/concatenation, architecture and transfer-learning theory |
| 7 | Mock A | Two Unit 4 numerical choices and Unit 3 theory; full answers directly below each question |
| 8 | Mock B and final revision | Mixed Unit 3/4 numerical choices; review both units' theory and redo incorrect calculations |
These are short study blocks, not eight full days. The exam is on 13 October; spend the remaining time on the Unit 3–4 blocks you have not completed. Do not spend this MT2 preparation period on the extra RNN material retained in TEE.