Coursera - Machine Learning by Andrew Ng

https://www.coursera.org/learn/machine-learning

Exercise 4: Neural Networks Learning

In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.
To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise.
You can also find instructions for installing Octave/MATLAB in the \En- vironment Setup Instructions" of the course website.

Files included in this exercise

ex4.m - Octave/MATLAB script that steps you through the exercise
ex4data1.mat - Training set of hand-written digits
ex4weights.mat - Neural network parameters for exercise 4
submit.m - Submission script that sends your solutions to our servers
displayData.m - Function to help visualize the dataset
fmincg.m - Function minimization routine (similar to fminunc)
sigmoid.m - Sigmoid function
computeNumericalGradient.m - Numerically compute gradients
checkNNGradients.m - Function to help check your gradients
debugInitializeWeights.m - Function for initializing weights
predict.m - Neural network prediction function
[?] sigmoidGradient.m - Compute the gradient of the sigmoid function
[?] randInitializeWeights.m - Randomly initialize weights
[?] nnCostFunction.m - Neural network cost function
? indicates files you will need to complete