Data visualization in neural networks involves using graphical techniques to represent data and model performance. It helps users understand how the network operates and makes decisions by presenting key metrics in a visual format. By visualizing metrics like loss and accuracy during training, users can monitor the model’s learning progress and detect issues like overfitting, where the model performs well on training data but poorly on new data. Data visualization tools can also illustrate the network’s architecture, showing the layers and neuron connections, providing a clearer view of the model’s structure and complexity. This makes it easier to analyze and optimize the neural network.
