Programming Exercise 3:
Multi-class Classification and Neural Networks
Machine Learning
Introduction
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, 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 to change to this directory before starting
this exercise.
Files included in this exercise
ex3.m - Octave script that will help step you through part 1
ex3 nn.m - Octave script that will help step you through part 2
ex3data1.mat - Training set of hand-written digits
ex3weights.mat - Initial weights for the neural network exercise
submitWeb.m - Alternative submission script
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
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