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Objective : The assignment aims at familiarizing you with numpy and the basics of ML and

feed-forward neural networks in python. We will be working mostly on the ipython notebooks

for the assignment. You can refer http://cs231n.github.io/ipython-tutorial/ for installing the

required software for working with ipython notebooks and getting started with it.

Assignment 1a : Multiple linear regression model

This part of the assignment consist of a python notebook that depicts the outline of using

multiple linear regression to learn a linear function to map input data to the output. The

function should be learned with the objective to minimize the Mean Square Error (MSE)

loss between the predicted output and the ground truth data.

The dataset for training and testing is provided in the assignment itself (‘Concrete_Data.csv’)

Assignment 1b : Feedforward Neural Network model

This part of the assignment consists of learning a feedforward neural network to classify

three types of flowers on the basis of certain input features. As in the previous assignment,

the objective is to decrease the categorical cross-entropy loss between the predicted

output and the ground truth data.

The dataset for training and testing is provided in the assignment itself (‘Iris_Data.csv‘)

For both the two assignments, you have to fill all the missing parts which is indicated using

question marks (?). Please don’t declare any function other than given in the notebook. The

functions have their usual meanings and is described below in brief. Some of the functions

are partially implemented and some of them are left for you to complete it.

Since the objectives are similar, we describe the common functions you need to write for

both these cases (i.e Assignment 1a and 1b).

● shuffle_dataset() : Simple function to shuffle the X and Y dataset. The dataset has to

be shuffled in place. (0.5 + 0.5)

● __init__ () : Initialize the linear regression and the neural network models by

specifying the biases and the weight matrices (2 + 5)

● forward() : Perform a forward pass by taking the entire dataset as the input for both

the models and calculates the output. Compute the predicted scores (for the LR

model) and labels ( for the NN models). (3 + 8 )

● backward() : Computes the gradients for every parameter of the network and update

the corresponding parameter using gradient descent step. This is also called a

backward pass as a prediction has been made and now the network is updated

accordingly, so as to minimize the loss function. (5 + 10)

● MSE_loss() : It computes the Mean Squared Loss between the predicted output and

ground truth data. (3)

● crossEntropy_loss(): It computes the categorical cross entropy loss between the

predicted output and ground truth data. (5)

● Accuracy: Computes the accuracy between the predicted and actual values for the

NN model (3).

● Compute the values of the following:

○ Final training loss (1+1)

○ Final test loss (1+1)

○ Final accuracy of the NN model (1)

Submission :

You have to submit two ipython notebooks with the .ipynb extension for the two

assignments . Please make sure that all the cells are running. The ipynb notebooks with the

skeleton code are given for your convenience. Please make the requisite changes there and

submit. Outline of the results mentioned above has been implemented in the notebook, you

just have to complete the blanks.

Please submit the completed file named as Assignment1a_

Assignment1b_

No report is required.

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