Deep neural network training involves understanding exploding and vanishing gradients. Exploding gradients become large, causing divergence, while vanishing gradients lead to slow convergence. These affect training times, convergence, and model performance. Techniques like weight initialization, activation functions (e.g., ReLU), batch normalization, gradient clipping, and residual connections mitigate these issues.