Transfer Learning with Keras

Transfer Learning with Keras

Now that we've covered image tensors, preprocessing, and basic computer vision in Deep Learning and Computer Vision, we are ready for a very practical question:

Do we really need to train every vision model from scratch?

Usually, no.

Training a strong image model from zero can require millions of images, expensive GPUs, and many experiment cycles. Transfer learning gives us a shortcut: start with a model that already learned useful visual patterns, then adapt it to our smaller task.

For a software engineer, think of this like importing a battle-tested library instead of rewriting the whole package yourself. You still customize the final behavior, but you reuse the hard-won foundation.