Week – 3 Lesson Quiz of Launching into Machine Learning

ByAdmin July 8, 2021

Week – 3 Lesson Quiz of Launching into Machine Learning

Gradient descent is an optimization algorithm used to maximize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

In machine learning, we use gradient descent to determine if our model labels needs to be de-optimized.

All of the above

Batch gradient descent, also called vanilla gradient descent, calculates the error for each example within the training dataset, but only before all training examples have been evaluated does the model get updated.

Batch gradient descent, also called vanilla gradient descent, calculates the gain for each example within the training dataset, but only before all training examples have been evaluated does the model get updated. This whole process is like a cycle and it’s called a training epoch.

Batch gradient descent, also called vanilla gradient descent, calculates the error for each example within the training dataset, but only after all training examples have been evaluated does the model get updated. This whole process is like a cycle and it’s called a training epoch.

None of the above

Batch Gradient Descent

Stochastic Gradient Descent

Mini Batch Gradient Descent

None of the above

Batch gradient descent

Gradient descent

Mini-batch gradient descent

None of the above

________________: Parameters are updated after computing the gradient of error with respect to the entire training set
________________: Parameters are updated after computing the gradient of error with respect to a single training example
________________: Parameters are updated after computing the gradient of error with respect to a subset of the training set

1 point

Batch Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent

:Mini-Batch Gradient Descent, Stochastic Gradient Descent, Batch Gradient Descent

Mini Batch Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent

None of the above

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