Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Implementation Miniba

This video tutorial covers the implementation of mini batch gradient descent, a hybrid of stochastic and batch gradient descent. It explains the importance of batch size and demonstrates the coding process, including debugging and optimizing the code for better performance. The tutorial also discusses the challenges of parameter tuning in deep neural networks and the benefits of using frameworks like Torch for efficient and bug-free coding.

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

What is the significance of the batch size in mini batch gradient descent?

How do you determine the accurate batch size for mini batch gradient descent?

What happens if the total number of examples (N) 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 understand the implementation of neural networks from scratch before using frameworks?