The exploding gradient problem is a significant challenge encountered during the training of deep neural networks. It occurs when large error gradients accumulate, leading to excessively large updates to the model’s weights. This instability can prevent the model from effectively learning from the training data, resulting in poor performance. The problem is particularly prevalent in deep architectures, where the gradients can grow exponentially as they propagate back through the layers. Understanding and addressing the exploding gradient problem is crucial for developing robust neural network models, ensuring stable training, and achieving better predictive accuracy.