Common steps for pre-processing a new dataset are:
- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)
- Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1)
- "Standardize" the data
You've implemented several functions that:
- Initialize (w,b)
- Optimize the loss iteratively to learn parameters (w,b):
- computing the cost and its gradient
- updating the parameters using gradient descent
- Use the learned (w,b) to predict the labels for a given set of examples
What to remember from this assignment (cat classification):
- Preprocessing the dataset is important.
- You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().
- Tuning the learning rate (which is an example of a "hyperparameter") can make a big difference to the algorithm.