The video tutorial explains the concept of random restarts in training models to overcome local minima issues. It discusses the difference between local and global minima and the challenges faced when using random restarts to find the global minimum. The tutorial suggests using a sufficient number of random restarts to approximate the global minimum, emphasizing the importance of error reduction in training.

What is the primary purpose of using random restarts in training?

• To avoid overfitting the model

• To ensure the training process is faster

• To find different local minima and potentially reduce error

• To increase the complexity of the model

What is a significant challenge when using random restarts to find the global minimum?

• It only works for linear models

• It cannot prove that a found minimum is the global minimum

• It requires a lot of computational resources

• It always leads to the global minimum

Why might random restarts not guarantee finding the global minimum?

• Because the global minimum is always at the start

• Due to the presence of many local minima

• Because it only works with small datasets

• It is not a valid technique in machine learning

What is a suggested method to approximate the global minimum when using random restarts?

• Ignore the errors and choose randomly

• Use a sufficient number of random restarts and choose the lowest error

• Select the minimum with the highest error

• Use only one random restart

How can the law of approximation help in finding the global minimum?

• By providing a mathematical proof of the global minimum

• By eliminating the need for random restarts

• By allowing the use of a limited number of restarts to approximate the global minimum

• By ensuring the first minimum found is the global minimum