Debugging tools help developers identify and fix issues in their models during the training and testing phases. When a neural network doesn’t perform as expected, debugging tools provide insights into what might be going wrong. These tools allow users to monitor the model's behavior, track the flow of data, and visualize the outputs at different stages of the training process. For instance, they can show how the loss (error) changes over time, helping to pinpoint if the model is learning effectively or if it's stuck. Besides, debugging tools can help identify problems like overfitting, where the model learns too much from the training data but struggles with new data. They may also provide features to test various inputs and understand how the model reacts.
