Week 2 - Logistic Regression with a Neural Network mindset

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.