Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Gradient Descent Stoc

The video tutorial covers different gradient descent methods: stochastic, mini batch, and batch gradient descent. It explains the bias term's role in neural networks, allowing hyperplanes to be positioned arbitrarily. The tutorial details how each gradient descent method works, their benefits, drawbacks, and computational requirements. Mini batch gradient descent is highlighted as a practical compromise. The video concludes with a preview of an animation and coding demonstration in the next video.

In what scenarios might omitting the bias term be acceptable?

What are the computational resource implications of using batch gradient descent on large datasets?

Describe the advantages of using mini-batch gradient descent.