Exploding gradient problem

The exploding gradient problem arises in neural networks, especially in deep learning, when gradients used in backpropagation become excessively large. This results in unstable training, with network weights undergoing extreme updates, leading to a failure in model convergence. It's particularly problematic in networks with many layers or long-range dependencies, as in Recurrent Neural Networks (RNNs). Gradients can grow exponentially through layers, causing instability. Solutions include gradient clipping, which caps the gradient to prevent large updates, and strategic weight initialization. Proper choice of layers and activation functions also helps in mitigating this issue, ensuring smoother training processes.