Deep Learning - Computer Vision for Beginners Using PyTorch - AutoGrad in a Loop

Deep Learning - Computer Vision for Beginners Using PyTorch - AutoGrad in a Loop

Assessment

Interactive Video

Information Technology (IT), Architecture, Mathematics

University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

This video tutorial explains how to implement gradient descent from scratch using PyTorch's autograph feature. It covers setting up a simple model with random integer inputs, defining hyperparameters, and using squared error loss. The tutorial demonstrates computing gradients, updating parameters, and finalizing the model to achieve results close to the true parameter values.

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5 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of using the autograd feature in PyTorch?

To manually calculate gradients

To generate random numbers

To automatically compute gradients

To initialize model parameters

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the equation used for generating the true values of Y in the model?

Y = 2.5X + 10

Y = 3.0X + 15

Y = 1.8X + 32

Y = 0.5X + 20

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it important to set 'requires_grad' to true for model parameters?

To increase the learning rate

To initialize parameters to zero

To enable gradient calculation

To prevent gradient calculation

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of using 'torch.no_grad()' in the code?

To compute gradients

To increase the learning rate

To initialize random numbers

To perform mathematical operations without computing gradients

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What were the final values of the parameters W and B after training?

W = 1.0, B = 32

W = 2.0, B = 35

W = 1.7999, B = 31.995

W = 1.5, B = 30

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