Greg discusses the complexities of training recurrent neural networks, particularly in speech recognition, where accumulated errors can lead to significant challenges. He highlights the issues of vanishing and exploding gradients, especially when using half precision, which can cause information loss due to the disparity in magnitude between gradients and weights. Adjustments were necessary to effectively manage these precision-related problems during training.
