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

The video tutorial covers the concept of backpropagation in neural networks, explaining how errors are corrected through the gradient descent process. It discusses the role of automatic differentiation in simplifying the computation of gradients and provides a practical example of implementing neural network learning using PyTorch. The tutorial also includes a demonstration of defining a sigmoid activation function and using different batch sizes in stochastic gradient descent.

What is the process by which the error is back propagated in a neural network?

How are the weights updated during the backpropagation process?

What is the significance of the learning rate in the weight update formula?

Can you explain the concept of gradient descent in the context of neural networks?

What is the role of the sigmoid activation function in a neural network?