Reinforcement Learning and Deep RL Python Theory and Projects - DNN Gradient Descent Exercise Solution

The video tutorial explains why the negative gradient direction is chosen for minimizing loss functions. It discusses the mathematical proof that shows the negative gradient is the most effective direction for rapid minimization. The tutorial also covers the importance of the learning rate in gradient descent, highlighting the balance between theoretical guarantees and practical concerns. Adjusting the learning rate is crucial for optimizing the algorithm's performance.

Why is the negative gradient direction chosen for minimizing a loss function?

• It is the only direction that increases the function.

• It is the direction that maximizes the function most rapidly.

• It leads to a local maximum.

• It is the direction that minimizes the function most rapidly.

What is the significance of taking a small step (alpha) in gradient descent?

• It ensures the function increases rapidly.

• It guarantees the function decays the most.

• It allows the function to remain constant.

• It causes the function to oscillate.

What is a potential issue with using a very small learning rate in practice?

• It can result in immediate convergence.

• It may lead to rapid convergence.

• It may take a long time to reach the minimum.

• It can cause the algorithm to overshoot the minimum.

Why might one choose to increase the learning rate in gradient descent?

• To guarantee reaching the global minimum.

• To speed up the convergence process.

• To ensure the function decays the most.

• To avoid any changes in the function value.

What is the study of adapting the learning rate between iterations called?

• Gradient ascent

• Learning rate stabilization

• Optimizer development

• Function maximization