Keras Training Workflow

Keras Training Workflow

Now that we've covered TensorFlow tensors, devices, and the Keras Sequential API in Practical Introduction to TensorFlow, we are ready for the part you will repeat constantly in real deep learning projects: the training workflow.

For a software engineer, think of Keras as a high-level training runtime. You define a model, configure how it should learn, run the training loop, inspect results, and then use or save the trained model.

The core loop is:

prepare data → build model → compile → fit → evaluate → predict/save

This lesson closes the Deep Learning Stack section by making that loop feel concrete. In the next section, we will apply the same workflow to image data in Deep Learning and Computer Vision.