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Multi-class Classification and Neural Networks Solution

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 “En- vironment 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 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

 

Throughout the exercise, you will be using the scripts ex3.m and ex3 nn.m. These scripts set up the dataset for the problems and make calls to functions that  you will write.  You do not need to modify these scripts.  You are only required  to modify functions  in other  files, by following the  instructions  in this assignment.

 

 

Submission and Grading

 

After completing this assignment, be sure to use the submit function to sub- mit your solutions to our servers.  The following is a breakdown of how each part  of this exercise is scored.

 

Part
Submitted File
Points
Regularized Logisic Regression
lrCostFunction.m
30 points
One-vs-all classifier training

One-vs-all classifier prediction
oneVsAll.m

predictOneVsAll.m
20 points

20 points
Neural Network Prediction  Function
predict.m
30 points
Total  Points
 
100 points
You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.

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