Building your Deep Neural Network: Step by Step

Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!

• In this notebook, you will implement all the functions required to build a deep neural network.
• In the next assignment, you will use these functions to build a deep neural network for image classification.

After this assignment you will be able to:

• Use non-linear units like ReLU to improve your model
• Build a deeper neural network (with more than 1 hidden layer)
• Implement an easy-to-use neural network class

Notation:

• Superscript [l] denotes a quantity associated with the l^th layer.
  • Example: a^[L] is the L^th layer activation. W^[L]and b^[L]are the L^th layer parameters.
• Superscript (i) denotes a quantity associated with the i^th example.
  • Example: x⁽ⁱ⁾ is the i^th training example.
• Lowerscript i denotes the i^th entry（项） of a vector.
  • Example: a_i^[l]denotes the i^th entry of the l^th layer‘s activations.

Let‘s get started!