Practice Gradient Descent Variants (8.3.2) - Deep Learning and Neural Networks

Practice: Gradient Descent Variants

Lesson 8.3.2Deep Learning and Neural Networks

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10 questions

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6 sample questions

Question 1.
Define Batch Gradient Descent.
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Consider how many examples are used at once.
Question 2.
What is the main advantage of Stochastic Gradient Descent?
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Think about the speed of weight updates.
3Question 3.
What is the primary characteristic of Batch Gradient Descent?
- Updates using entire dataset
- Updates after each example
- Combines both methods
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Think about how many examples are used to compute the gradient.
4Question 4.
True or False: Stochastic Gradient Descent is more stable than Batch Gradient Descent.
- True
- False
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Consider the nature of updates from individual samples.
Question 5.
Analyze a dataset subjected to Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent. Could you identify the trade-offs in convergence time, stability, and computational efficiency?
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Consider how each method processes training examples.
Question 6.
In an experiment, Neural Network A utilized Adam Optimizer while Neural Network B used Adagrad. Discuss the expected performance on sparse data versus dense data.
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Evaluate the characteristics of training data while considering optimizer capabilities.

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