Week 1: Introduction to Deep Learning

Week 2 : Neural Network Basics

Week 3: Shallow Neural Networks

Week 4: Deep L-layer Neural Network

### 3.3 - General Methodology

As usual, you'll follow 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

References:

Week 2:

• GitHub: Implementing a Neural Network from Scratch in Python – An Introduction (Denny Britz, 2015)
• Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? (Stack Exchange)

Week 3:

• CS231n: Convolutional Neural Networks for Visual Recognition (Stanford University)

Week 4:

Autoreload of modules in IPython (Stack Overflow)