Deep Learning - Deep Neural Network for Beginners Using Python - Random Restart Solution

The video tutorial explains the concept of random restarts in training models to find optimal solutions. It discusses the challenges of local and global minima in optimization problems and the limitations of using random restarts to find global minima. The tutorial suggests using a sufficient number of random restarts to approximate the global minimum, emphasizing the importance of approximation in optimization.

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

• To ensure the model runs faster

• To avoid overfitting the model

• To increase the complexity of the model

• To find different local minima and reduce error

What is a significant challenge when dealing with multiple local minima?

• Identifying the global minimum among many local minima

• Ensuring all local minima are of the same depth

• Reducing the number of local minima

• Increasing the number of random restarts

Why is it difficult to prove that a found minimum is the global minimum?

• Because there might be a deeper minimum that hasn't been discovered

• Because all minima are of equal depth

• Because the global minimum is always at the start

• Because the global minimum is always the first one found

What is a suggested method to approximate the global minimum?

• Using only local minima for training

• Avoiding random restarts altogether

• Using a large number of random restarts and selecting the lowest error

• Using a single random restart

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

• By reducing the number of random restarts needed

• By increasing the number of local minima

• By allowing the assumption that the lowest error found is the global minimum

• By ensuring all errors are zero