Keras
Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.
Deep Learning with R, Third Edition
Keras is an open-source deep learning API for building and training neural networks.
- How it is used
- Provides a high-level interface for defining, training, evaluating, and using neural networks.
- How deep it goes
- Works through a complete classifier, then extends to model-building options, custom training steps, and distributed-training configuration.
- Examples
- Uses an R keras3 MNIST classifier, backend selection, custom train_step() implementations, and JAX distribution settings.
- What you can do after
- Apply it via keras3 to build, train, evaluate, and predict with a model, then choose more flexible workflows as needed.
Read how Deep Learning with R, Third Edition explains Keras →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Keras is an open-source deep learning API for Python that provides interfaces for building and training neural-network models.
- How it is used
- Implements deep-learning solutions for tabular price-prediction and classification problems.
- How deep it goes
- Explains model layers, fit arguments, callbacks, persistence, evaluation, and repeated-run validation without detailing TensorFlow internals.
- Examples
- Uses Airbnb NYC listing-price prediction and Kuala Lumpur real-estate price classification.
- What you can do after
- Apply it via Keras to define, train, checkpoint, load, and evaluate tabular deep-learning models.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains Keras →
Deep Learning with Python, Third Edition
The programming interface of Keras that exposes multiple model-construction workflows and lets them interoperate.
- How it is used
- Provides a high-level interface for assembling, training, predicting with, and evaluating neural networks.
- How deep it goes
- Works through an end-to-end model and sketches custom Layer implementation, while deferring fuller explanation of build() and call().
- Examples
- Uses MNIST digit classification and a custom dense layer to show both ready-made and subclassed components.
- What you can do after
- Apply it via Keras to train and evaluate a layered model, then choose a more customized workflow when needed.
Read how Deep Learning with Python, Third Edition explains Keras →
Grokking Machine Learning
An open-source deep-learning library providing high-level APIs for constructing and training neural networks.
- How it is used
- Uses Keras to put neural-network theory into practice through classification examples.
- How deep it goes
- Demonstrates preprocessing, layer construction, compilation, training, and prediction without detailing library internals.
- Examples
- Uses a two-feature circular dataset and MNIST handwritten digits to demonstrate classification.
- What you can do after
- Build from scratch and train classifiers for nonlinear points and handwritten digits.
Distributed Machine Learning Patterns
Keras is a high-level deep-learning API for defining, training, evaluating, and exporting neural-network models, commonly used through TensorFlow as tf.keras.
- How it is used
- Uses a high-level TensorFlow API to move from dataset loading through neural-network training, evaluation, and persistence.
- How deep it goes
- Defines layers, compilation, fitting, evaluation, saving, and tuning, but does not descend into TensorFlow internals.
- Examples
- Demonstrates normalized MNIST images, a Sequential classifier, model persistence, and Keras Tuner Hyperband with early stopping.
- What you can do after
- Build it from scratch: create, train, evaluate, save, reload, and tune a TensorFlow neural-network model.
Read how Distributed Machine Learning Patterns explains Keras →
Evolutionary Deep Learning: Genetic algorithms and neural networks
Keras is an open-source deep-learning library providing high-level APIs for constructing and training neural networks.
- How it is used
- Deploys high-level neural-network construction and training for classification, optimizer comparison, and neuroevolution experiments.
- How deep it goes
- Descends through executable model definition, compilation, fitting, parameter counting, and weight replacement, but not framework internals.
- Examples
- Uses moons and circles classification datasets, Dense-layer models, loss plots, prediction displays, and evolved model weights.
- What you can do after
- Builds and trains a Keras classifier, inspects its parameters, and integrates genetic-algorithm weight optimization.
Read how Evolutionary Deep Learning: Genetic algorithms and neural networks explains Keras →
Related concepts
- TensorFlowuses · TensorFlow is an open-source software platform for numerical computation and machine learning, with tools for developing, training, and deploying models.
- autoencoderused by · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
- deep learninguses · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
- PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
- backenduses · A backend is a software component or subsystem that provides underlying processing, data access, execution, or services to clients or other components, typically behind a user-facing interface.
- backpropagationimplements · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
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