Which components are most useful when building custom neural networks?

The components that are most useful when building custom neural networks include loss functions, metrics, and optimizers, which are all essential for training models effectively. Loss functions measure how well the model's predictions match the actual data, guiding the optimizer in updating the model's parameters. Metrics help evaluate the performance of the model during and after training, offering insights into how the model is performing beyond just the training loss. Optimizers are responsible for adjusting the model parameters based on the gradients computed from the loss function, allowing the network to learn and improve over time.

This combination provides a foundational structure for developing and refining neural networks, making it crucial for anyone working in machine learning to understand how to utilize these components effectively.