Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Implementation Stocha

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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of defining input dimensions and data points in the initial setup?
To define the loss function
To set the number of epochs
To initialize the learning rate
To determine the size of the neural network
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the training function, what is the role of the 'epoch' variable?
To store the loss values
To iterate over the data points
To update the learning rate
To track the number of complete passes over the dataset
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to compute the loss after each example in stochastic gradient descent?
To reduce computation time
To decrease the number of epochs
To increase the learning rate
To update weights immediately and improve convergence
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What common error is highlighted during the debugging process of the forward step?
Incorrect learning rate
Undefined loss function
Index out of range
Mismatched data types
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the significance of setting gradients to zero after updating weights?
To double the weight updates
To ensure the loss is minimized
To increase the learning rate
To prevent accumulation of gradients from previous iterations
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is random selection of data points important in stochastic gradient descent?
To ensure faster computation
To simulate sampling with replacement
To avoid overfitting
To reduce the number of epochs
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What practical strategy is often used to mimic random selection in SGD?
Increasing the learning rate
Reducing the number of data points
Using a fixed sequence of data points
Shuffling the data before each epoch
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