Deep Learning with Keras 3

Patterns and best practices based on Deep Learning with Python, 2nd Edition by François Chollet, updated for Keras 3 (Multi-Backend).

Core Workflow

1. Prepare Data: Normalize, split train/val/test, create tf.data.Dataset
2. Build Model: Sequential, Functional, or Subclassing API
3. Compile: model.compile(optimizer, loss, metrics)
4. Train: model.fit(data, epochs, validation_data, callbacks)
5. Evaluate: model.evaluate(test_data)

Model Building APIs

Sequential - Simple stack of layers:

model = keras.Sequential([
    layers.Dense(64, activation="relu"),
    layers.Dense(10, activation="softmax")