Conclusion

A training loop is the first complete learning system in this path.

The pieces are now connected:

• a model computes predictions
• a loss measures error
• backpropagation computes gradients
• an optimizer updates parameters
• metrics show whether training is behaving

This chapter also gave names to the practical controls: batches, epochs, steps, learning rate, momentum, adaptive optimizers, schedules, and clipping.

The next chapter asks why even a correct loop can still fail. Initialization, scale, and normalization decide whether signals and gradients remain usable as networks get deeper.