Discuss the challenges associated with training deep neural networks and explain techniques like batch normalization and residual connections used to address these challenges.

Training deep neural networks poses several challenges, including the vanishing gradient problem, overfitting, and computational complexity. These challenges can hinder the convergence of the network during training and limit its ability to generalize well to unseen data. To address these issues, techniques like batch normalization and residual connections have been introduced. 1. Vanishing Gradient Problem: Deep neural networks often suffer from the vanishing gradient problem, where the gradients diminish exponentially as they propagate backward through multiple layers. This can lead to slow convergence and difficulty in training deeper networks. The vanishing gradient problem occurs due to the nature of activation functions, such as the sigmoid function, which saturate for large input values. 2. Overfitting: Deep neural networks are prone to overfitting, which refers to the phenomenon where the model becomes too specialized to the training data and fails to generalize well to new, unseen data. Overfitting occurs when the model becomes too complex relative to the available training data, leading to the network capturing noise or irrelevant patterns in the data. To address these challenges, several techniques have been developed: 1. Batch Normalization: Batch normalization is a technique that helps mitiga....