Practice Experiment With Different Optimizers (lab.4) - Introduction to Deep Learning (Weeks 11)
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Experiment with Different Optimizers

Practice - Experiment with Different Optimizers

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary purpose of an optimizer in neural networks?

💡 Hint: Think about how we learn from mistakes.

Question 2 Easy

Describe one disadvantage of using Stochastic Gradient Descent.

💡 Hint: Consider how updates occur.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does an optimizer do in a neural network?

Adjusts model architecture
Modifies weights and biases
Preprocesses input data

💡 Hint: Focus on what helps reduce errors in predictions.

Question 2

True or False: Adam optimizer is known for requiring extensive hyperparameter tuning.

True
False

💡 Hint: Consider how adaptive it is.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Choose an optimizer for a high-dimensional image classification task and justify your choice. Discuss the advantages and disadvantages.

💡 Hint: Think about the data's complexity.

Challenge 2 Hard

Explain how you would approach training a neural network for a non-stationary objective. Which optimizer would you choose and why?

💡 Hint: Focus on how the optimizer reacts to changing data.

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Reference links

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