Keras

6.3easy start · #16 of 36
in Deep Learning Software

Free to practise onnot on record
Free trialnot on record
Well documentedyes
Runs where you workyes

Runs on Linux, Mac, Windows.

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Keras is ranked #16 of 36 in deep learning software on The Geeks Club. It runs on Linux, macOS, Windows.

Compared on deep learning software

Free plan: Yes
Training mode: local
Deployment targets: multiple
GPU acceleration: Yes
Distributed training: Yes
Supported languages: Python
Model formats: Keras (.keras), TensorFlow SavedModel, ONNX, OpenVINO, LiteRT, PyTorch ExportedProgram

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The record

Product: Keras is a Python deep learning API focused on readable, maintainable code and fast model iteration.
Frameworks: Keras 3 runs on JAX, TensorFlow, or PyTorch, and supports OpenVINO for inference only.
Installation: Keras installs from PyPI with pip install --upgrade keras; using Keras 3 also requires installing a backend framework.
Model building: The API includes layers, metrics, loss functions, optimizers, callbacks, training and evaluation loops, and saving and serialization tools.
Data inputs: Keras 3 training, evaluation, and prediction routines support tf.data.Dataset, PyTorch DataLoader, NumPy arrays, and Pandas dataframes.
Model interoperability: Keras 3 models can be used as PyTorch modules, exported as TensorFlow SavedModels, or instantiated as stateless JAX functions.
Distribution: The distribution API supports data and model parallelism and is currently implemented for the JAX backend.
Pretrained models: KerasHub provides Keras 3 implementations of popular architectures and pretrained checkpoints on Kaggle Models for training and inference.
Examples: The getting-started page offers over 150 example notebooks covering computer vision, natural language processing, and generative AI.
Community support: Keras provides a Google Group, community meetings, Discord, and a Google AI Forum for discussion and updates.
Contributions: The Keras site invites code, ideas, and feedback and links to its roadmap, contribution guide, and GitHub repository.
Requirement: Keras 3 requires a separately installed backend framework, and the backend must be configured before importing Keras.
Intended users: Keras describes its audience as machine learning engineers and presents guides and examples for model development across common ML use cases.
Purpose: Keras is a Python deep learning API designed to make model development concise, readable, and easier to debug.

Where it ranks on The Geeks Club

Best Deep Learning Software in 2026#16 of 36

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Sources

keras.io· checked 30 Sept 2026
keras.io/keras_3/· checked 30 Sept 2026
keras.io/getting_started/· checked 30 Sept 2026
keras.io/getting_started/intro_to_keras_for_engi· checked 30 Sept 2026
keras.io/keras_tuner/· checked 30 Sept 2026
keras.io/about/· checked 30 Sept 2026