#1




Homework for Neural Network
Professor Yaser,
In lecture 9 Q & A. You mentioned that you used to have a homework programming assignment for creating a Neural Network, but that was removed. Could you please post that homework assignment. It would be very helpful. Thank you 
#2




Re: Homework for Neural Network
Here are the old homework problems (not required in this course, and there is no technical support).
Backpropagation: Following the class notes, implement the backpropagation algorithm that takes as input a network architecture () and a set of examples where and , and produces as output the network weights. The algorithm should perform gradient descent on one example at a time, but should also keep track of the average error for all the examples in each epoch. Try your algorithm on the data set in http://work.caltech.edu/train.dat (the first two columns are the input and the third column is the output). Test the convergence behavior for architectures with one hidden layer () and 1 to 5 neurons (), with combinations of the following parameters: (i) The initial weight values chosen independently and randomly from the range (0.02,0.02), the range (0.2,0.2), or the range (2,2). (ii) The learning rate fixed at , or . (iii) Sufficient number of epochs to get the training error to converge (within reason). Turn in your code and a single parameter combination that resulted in good convergence for the above architectures. Generalization: Using your backpropagation program and data from the above problem, train different neural networks with (an input layer, one `hidden' layer, and an output layer) where the number of neurons in the hidden layer is 1, 2, 3, 4, or 5. Use the following outofsample data to test your networks: http://work.caltech.edu/test.dat Plot the training and test errors for each network as a function of the epoch number (hence the `intermediate' networks are evaluated using the test data, but the test data is not used in the backpropagation). Repeat the experiment by reversing the roles of the training and test sets (you may need to readjust the parameter combination from the previous problem), and plot the training and test errors again. Briefly analyze the results you get.
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#3




Re: Homework for Neural Network
Thank you Professor Yaser.

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neural network 
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