Practice - Optimization with Gradient Descent
Practice Questions
Test your understanding with targeted questions
What is the main purpose of gradient descent?
💡 Hint: Think about what you are trying to achieve when training a model.
Define learning rate in the context of gradient descent.
💡 Hint: What does learning rate control in the context of updating weights?
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the goal of gradient descent?
💡 Hint: Remember the function you are actually trying to improve in a model.
Stochastic Gradient Descent updates weights using:
💡 Hint: Think about how quickly you can learn from just one piece of information.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.