Deep Learning - Deep Neural Network for Beginners Using Python - Vanishing Gradient Problem

The video tutorial explains the vanishing gradient problem in neural networks, particularly when using the sigmoid activation function. It describes how the gradient becomes very small at extreme values of the sigmoid function, leading to slow or ineffective learning during backpropagation. The tutorial discusses the impact of small derivatives on weight updates and the learning rate, highlighting the challenges in reaching the minima efficiently. The vanishing gradient problem can significantly hinder the training process, making it difficult for neural networks to learn effectively.

Discuss the implications of taking very small steps during gradient descent due to the vanishing gradient problem.

What potential solutions can be considered to address the vanishing gradient problem?