1. Biological inspiration (neuron analogy)

2. Perceptron

3. Activation functions (sigmoid, tanh, ReLU, GELU, softmax)

4. Multi-layer perceptron (MLP)

5. Forward propagation

6. Loss functions (MSE, cross-entropy, NLL)

7. Backpropagation (chain rule in action)

8. Weight initialization

9. Batch normalization

10. Dropout regularization

11. Epochs, batches, iterations