Deep Learning · TEE · section

Recommended reading order

TEE preparation and examination plan

1. Neural-network foundations: learning tasks, data partitions, neuron models, perceptrons, activations, losses and backpropagation.

2. Optimization: gradient descent variants, momentum, Nesterov, AdaGrad, RMSProp and Adam, including their states and update conventions.

3. Regularization and generalization: overfitting, L1/L2 objectives, dropout, early stopping and remedies selected from training behavior.

4. CNNs: convolution and pooling operations, output dimensions, trainable parameter counts and complete network calculations.

5. CNN architectures: AlexNet, Inception, ResNet, bottlenecks and transfer learning.

6. Recurrent models: state equations, BPTT, vanishing/exploding gradients, LSTM and GRU.

7. Examination preparation: use the formula reference, complete solved questions and full TEE paper rehearsal.

Use the Mathematics revision chapter for notation, matrix operations, differentiation, probability and logarithms. It is a reference rather than the compulsory starting point.