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From AI-Toolkit
Comprehensive guide for Deep Learning with Keras 3 (Multi-Backend: JAX, TensorFlow, PyTorch). Use when building neural networks, CNNs for computer vision, RNNs/Transformers for NLP, time series forecasting, or generative models (VAEs, GANs). Covers model building (Sequential/Functional/Subclassing APIs), custom training loops, data augmentation, transfer learning, and production best practices.
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Patterns and best practices based on *Deep Learning with Python, 2nd Edition* by François Chollet, updated for Keras 3 (Multi-Backend).
Builds and trains neural networks using TensorFlow Keras API, including sequential models, CNNs for image classification like MNIST, with training, evaluation, and model saving.
Provides PyTorch patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
Provides access to pre-trained transformer models for NLP, vision, audio, and multimodal tasks. Supports inference, fine-tuning, and tasks like text generation, classification, question answering.
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Patterns and best practices based on Deep Learning with Python, 2nd Edition by François Chollet, updated for Keras 3 (Multi-Backend).
tf.data.Datasetmodel.compile(optimizer, loss, metrics)model.fit(data, epochs, validation_data, callbacks)model.evaluate(test_data)Sequential - Simple stack of layers:
model = keras.Sequential([
layers.Dense(64, activation="relu"),
layers.Dense(10, activation="softmax")
])
Functional - Multi-input/output, shared layers, non-linear topologies:
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:
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)
| 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)
| Topic | Reference | When to Use |
|---|---|---|
| Keras 3 Migration | keras3_changes.md | START HERE: Multi-backend setup, keras.ops, import keras |
| Fundamentals | basics.md | Overfitting, regularization, data prep, K-fold validation |
| Keras Deep Dive | keras_working.md | Custom metrics, callbacks, training loops, tf.function |
| Computer Vision | computer_vision.md | Convnets, data augmentation, transfer learning |
| Advanced CV | advanced_cv.md | Segmentation, ResNets, Xception, Grad-CAM |
| Time Series | timeseries.md | RNNs (LSTM/GRU), 1D convnets, forecasting |
| NLP & Transformers | nlp_transformers.md | Text processing, embeddings, Transformer encoder/decoder |
| Generative DL | generative_dl.md | Text generation, VAEs, GANs, style transfer |
| Best Practices | best_practices.md | KerasTuner, mixed precision, multi-GPU, TPU |
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)
| Script | Description |
|---|---|
| quick_train.py | Reusable training template with standard callbacks and history plotting |
| visualize_filters.py | Visualize convnet filter patterns via gradient ascent |