Practice Gradient Descent Variants - 8.3.2 | 8. Deep Learning and Neural Networks | Data Science Advance
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8.3.2 - Gradient Descent Variants

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define Batch Gradient Descent.

💡 Hint: Consider how many examples are used at once.

Question 2

Easy

What is the main advantage of Stochastic Gradient Descent?

💡 Hint: Think about the speed of weight updates.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary characteristic of Batch Gradient Descent?

  • Updates using entire dataset
  • Updates after each example
  • Combines both methods

💡 Hint: Think about how many examples are used to compute the gradient.

Question 2

True or False: Stochastic Gradient Descent is more stable than Batch Gradient Descent.

  • True
  • False

💡 Hint: Consider the nature of updates from individual samples.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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?

💡 Hint: Consider how each method processes training examples.

Question 2

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.

💡 Hint: Evaluate the characteristics of training data while considering optimizer capabilities.

Challenge and get performance evaluation