Coursera - Machine Learning by Andrew Ng

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

Exercise 3: Multi-class Classification and Neural Networks

In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize hand-written digits. Before starting 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 Environment Setup Instructions" of the course website.

Files included in this exercise

ex3.m - Octave/MATLAB script that steps you through part 1
ex3 nn.m - Octave/MATLAB script that steps you through part 2
ex3data1.mat - Training set of hand-written digits<\br> ex3weights.mat - Initial weights for the neural network exercise
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
[?] lrCostFunction.m - Logistic regression cost function
[?] oneVsAll.m - Train a one-vs-all multi-class classifier
[?] predictOneVsAll.m - Predict using a one-vs-all multi-class classifier
[?] predict.m - Neural network prediction function
? indicates files you will need to complete