- Part 1. Import Libraries, Loading and Preprocessing the Training and Testing Data.
- Part 2. Build the Neural Network, Forward and Backward Propagation.
- 2.1 Check the structure of data samples
- 2.2 Construct the model and implement the forward propagation
- Initialize the model parameters
- Define the activation function
- Construct the forward propagation function
- 2.3 Loss function computation and backward propagation
- Implement the loss function
- Implement the backward propagation
- Extended Reading: Gradient check using finite-difference approximation.
- Part 3. Training and Evaluation of Neural Network.
- 3.1 Training your network
- 3.2 Evaluate the performance of your model
- Part 4. Regularization and Hyperparameter Tuning.
- 4.1 Implement weight decay loss and backward propagation
- 4.2 Hyperparameter Tuning