# Recommended reading order

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/read/1/tee-c1-s1

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c1-s1/

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.