Neural Networks and Deep Learning Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.

Learning Objectives Build a logistic regression model structured as a shallow neural network Build the general architecture of a learning algorithm, including parameter initialization, cost function and gradient calculation, and optimization implemetation (gradient descent) Implement computationally efficient and highly vectorized versions of models Compute derivatives for logistic regression, using a backpropagation mindset Use Numpy functions and Numpy matrix/vector operations Work with iPython Notebooks Implement vectorization across multiple training examples Explain the concept of broadcasting