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Write python code for this :   Prepare a Dataset of 300 samples of Random Values of two Variables in range(1-1000). Let’s call these variables as X and Y such that X > Y. The Values of Remainder and Quotient is to be calculated for each sample (X, Y) pair such that Remainder = X % Y and Quotient = X / Y. Design a Neural Network Architecture and Pipeline that can take X and Y as input as input and Predict the values of Remainder and Quotient. You are to choose which Error Function you should be using. Derive the weight update Equations. Train the model for above generated Dataset for 10 epochs for all weights of the network using Python code from scratch and show the weight updates. Also, verify if this reduces the error or not.

Write python code for this :   Prepare a Dataset of 300 samples of Random Values of two Variables in range(1-1000). Let’s call these variables as X and Y such that X > Y. The Values of Remainder and Quotient is to be calculated for each sample (X, Y) pair such that Remainder = X % Y and Quotient = X / Y. Design a Neural Network Architecture and Pipeline that can take X and Y as input as input and Predict the values of Remainder and Quotient. You are to choose which Error Function you should be using. Derive the weight update Equations. Train the model for above generated Dataset for 10 epochs for all weights of the network using Python code from scratch and show the weight updates. Also, verify if this reduces the error or not.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Write python code for this :

 

Prepare a Dataset of 300 samples of Random Values of two Variables in range(1-1000). Let’s call these

variables as X and Y such that X > Y. The Values of Remainder and Quotient is to be calculated for each

sample (X, Y) pair such that Remainder = X % Y and Quotient = X / Y.

Design a Neural Network Architecture and Pipeline that can take X and Y as input as input and Predict the

values of Remainder and Quotient. You are to choose which Error Function you should be using. Derive

the weight update Equations.

Train the model for above generated Dataset for 10 epochs for all weights of the network using Python

code from scratch and show the weight updates. Also, verify if this reduces the error or not.

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