
Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation Stochastic Gradient Descent
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Information Technology (IT), Architecture
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University
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Practice Problem
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Hard
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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 a loss function in neural network training?
To determine the number of epochs
To set the learning rate
To measure the performance of the model
To initialize the weights
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of stochastic gradient descent, what does an epoch represent?
A random selection of data points
A single update of weights
A complete pass through the entire dataset
A fixed number of iterations
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to compute the average loss after each epoch?
To update the weights
To determine the number of data points
To evaluate the model's performance over the entire dataset
To adjust the learning rate
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of the forward step in neural network training?
To initialize the weights
To compute the predicted output
To update the gradients
To shuffle the data
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it necessary to set gradients to zero after each parameter update?
To shuffle the data
To increase the learning rate
To prevent accumulation of gradients from previous iterations
To decrease the number of epochs
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key theoretical requirement of stochastic gradient descent regarding data point selection?
Random selection without replacement
Sequential selection with replacement
Random selection with replacement
Sequential selection without replacement
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How do practitioners often handle data point selection in practice for stochastic gradient descent?
By selecting data points sequentially without shuffling
By randomly selecting data points with replacement
By shuffling data before each epoch
By using only the first data point repeatedly
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