deep-learning

# 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: ```python model = keras.Sequential([ layers.Dense(64, activation="relu"), layers.Dense(10, activation="softmax") ]) ``` **Functional** - Multi-input/output, shared layers, non-linear topologies: ```python inputs = keras.Input(shape=(64,)) x = layers.Dense(64, activation="relu")(inputs) outputs = layers.Dense(10, activation="softmax")(x) model = keras.Model(inputs=inputs, outputs=outputs) ``` **Subclassing** - Full flexibility with `call()` method: ```python class MyModel(keras.Model): def __init__(self): super().__init__() self.dense1 = layers.Dense(64, activation="relu") self.dense2 = layers.Dense(10, activation="softmax") def call(self, inputs): x = self.dense1(inputs) return self.dense2(x) ``` ## Quick Reference: Loss & Optimizer Selection | Task | Loss | Final Activation | |------|------|------------------| | Binary classification | `binary_crossentropy` | `sigmoid` | | Multiclass (one-hot) | `categorical_crossentropy` | `softmax` | | Multiclass (integers) | `sparse_categorical_crossentropy` | `softmax` | | Regression | `mse` or `mae` | None | **Optimizers**: `rmsprop` (default), `adam` (popular), `sgd` (with momentum for fine-tuning) ## Domain-Specific Guides | Topic | Reference | When to Use | |-------|-----------|-------------| | **Keras 3 Migration** | [keras3_changes.md](references/keras3_changes.md) | **START HERE**: Multi-backend setup, `keras.ops`, `import keras` | | **Fundamentals** | [basics.md](references/basics.md) | Overfitting, regularization, data prep, K-fold validation | | **Keras Deep Dive** | [keras_working.md](references/keras_working.md) | Custom metrics, callbacks, training loops, `tf.function` | | **Computer Vision** | [computer_vision.md](references/computer_vision.md) | Convnets, data augmentation, transfer learning | | **Advanced CV** | [advanced_cv.md](references/advanced_cv.md) | Segmentation, ResNets, Xception, Grad-CAM | | **Time Series** | [timeseries.md](references/timeseries.md) | RNNs (LSTM/GRU), 1D convnets, forecasting | | **NLP & Transformers** | [nlp_transformers.md](references/nlp_transformers.md) | Text processing, embeddings, Transformer encoder/decoder | | **Generative DL** | [generative_dl.md](references/generative_dl.md) | Text generation, VAEs, GANs, style transfer | | **Best Practices** | [best_practices.md](references/best_practices.md) | KerasTuner, mixed precision, multi-GPU, TPU | ## Essential Callbacks ```python callbacks = [ keras.callbacks.EarlyStopping(monitor="val_loss", patience=3), keras.callbacks.ModelCheckpoint("best.keras", save_best_only=True), keras.callbacks.TensorBoard(log_dir="./logs") ] model.fit(..., callbacks=callbacks) ``` ## Utility Scripts | Script | Description | |--------|-------------| | [quick_train.py](scripts/quick_train.py) | Reusable training template with standard callbacks and history plotting | | [visualize_filters.py](scripts/visualize_filters.py) | Visualize convnet filter patterns via gradient ascent |