User Friendly AI Tool
What is Keras?
Keras is a deep learning API designed for human beings, focusing on debugging speed, code elegance and conciseness, maintainability, and deployability. It helps make your codebase smaller, more readable, and easier to iterate on.
What backends does Keras support?
Keras uses a multi-backend approach that lets you work with JAX, TensorFlow, and PyTorch, enabling models to move seamlessly across these frameworks and to leverage the strengths of each ecosystem.
What can I build with Keras?
You can build models using the Functional API or by subclassing Keras layers. The Functional API and training/evaluation with built-in methods (like model.fit) are demonstrated in the guides, including example architectures with Conv2D, pooling, activations, and dense layers.
What is Keras Hub and what can I use it for?
KerasHub provides Keras 3 implementations of popular model architectures, paired with pretrained checkpoints available on Kaggle. Models can be used for both training and inference across TensorFlow, JAX, and PyTorch backends. Popular entries include GEMMA, LLAMA, STABLE DIFFUSION, and MISTRAL.
Has Keras 3.0 been released?
Yes, Keras 3.0 has been released.
Who uses Keras?
Keras is used by leading institutions and organizations such as CERN, NASA, NIH, and Waymo. It is also associated with platforms like Kaggle and HuggingFace to support ML developers in daily work.
How do I get started with Keras?
Start with the Get Started and Run Quickstart materials. You can learn the Functional API, then build and train models with the built-in methods like model.fit, following the guidance in the guides and examples.
Where can I find Keras docs, guides, and examples?
Keras provides API documentation, developer guides, and a library of examples. Guides cover the Functional API and training/evaluation workflows, with sections on computer vision, natural language processing, and generative deep learning.
What kinds of guides and examples does Keras provide?
The guides include The Functional API and Training & evaluation with the built-in methods. Examples span Computer Vision, Natural Language Processing, and Generative Deep Learning to illustrate common tasks and architectures.
How can I contribute to Keras or view the roadmap?
You can explore the roadmap and contribution guide, and visit the GitHub repository for contributions and collaborations.
How can I stay connected with the Keras community?
Stay connected by joining the Google Group, community meetings, Discord, and the Google AI Forum. Community resources and announcements are shared through these channels.