The vanishing gradient problem is a significant challenge encountered in training deep neural networks, particularly those with many layers. It arises during the backpropagation process, where gradients, which are essential for updating the network’s weights, become exceedingly small as they propagate backward through the layers. This leads to minimal weight updates in the earlier layers, hindering the network’s ability to learn effectively. Consequently, the model struggles to capture complex patterns in the data, ultimately affecting its performance. Understanding and addressing the vanishing gradient problem is crucial for developing robust deep learning models.