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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the main purpose of gradient descent?
💡 Hint: Think about what you are trying to achieve when training a model.
Question 2
Easy
Define learning rate in the context of gradient descent.
💡 Hint: What does learning rate control in the context of updating weights?
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 goal of gradient descent?
- Minimize loss
- Maximize accuracy
- Stabilize weights
💡 Hint: Remember the function you are actually trying to improve in a model.
Question 2
Stochastic Gradient Descent updates weights using:
- Entire dataset
- Batch of data
- Single data point
💡 Hint: Think about how quickly you can learn from just one piece of information.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
How would you adjust the gradient descent approach if you encounter oscillation in the loss during training?
💡 Hint: Think about what properties of weight updates could be helpful in slowing down convergence.
Question 2
Design a neural network structure that can effectively use both SGD and Mini-batch techniques. Discuss parameters.
💡 Hint: Consider what metrics might inform when to shift strategies effectively.
Challenge and get performance evaluation