Deep Neural Network for Image Classification

About dataset:

• a training set of m_train images labelled as cat (1) or non-cat (0)
• a test set of m_test images labelled as cat and non-cat
• each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB).

The Deep Learning methodology to build the model:

1. Initialize parameters / Define hyperparameters
2. Loop for num_iterations: a. Forward propagation b. Compute cost function c. Backward propagation d. Update parameters (using parameters, and grads from backprop)
3. Use trained parameters to predict labels