Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation Minibatch Gradient Descent

The video tutorial explains the implementation of mini batch gradient descent, a method combining stochastic and batch gradient descent. It covers setting up the function, parameters, and batch processing logic. Debugging and error fixing are demonstrated, followed by a discussion on parameter tuning and neural network implementation. The tutorial concludes with a look at using frameworks like Torch for more efficient coding.

What is mini batch gradient descent and how does it differ from batch and stochastic gradient descent?

Explain the significance of the batch size parameter in mini batch gradient descent.

How do you determine the number of batches in mini batch gradient descent?

What happens if the total number of examples is not completely divisible by the batch size?

Describe the process of updating parameters after each batch in mini batch gradient descent.

What are the advantages and disadvantages of having many parameters in a neural network?

Why is it beneficial to use frameworks for implementing neural networks?