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About Keras

Keras is a machine learning software from Keras that supports multi-framework development. It provides GET STARTED guides, a library of EXAMPLES, KERAS TUNER for model improvement, KERAS RS for reinforcement learning, and KERAS HUB for pre-trained models so developers can quickly implement machine learning solutions. Keras is designed to facilitate both beginners and experienced developers with comprehensive documentation, practical code examples, and an extensive set of features that allow for flexibility in model building and deployment. Key capabilities: model creation hyperparameter tuning reinforcement learning access to pre-trained models extensive documentation Best for: machine learning developers that need a flexible framework for building and testing models.

Keras Details

Vendor
Keras
Year Launched
N/A
Location
Google New York 111 8th Ave New York, NY 10011 United States
Deployment
cloud, on premise, windows, linux
Training Options
documentation, community
Countries Served
All Countries
Languages
English, Arabic, Chinese, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Turkish
Users
AI Researchers, Machine Learning Engineers, Data Scientists, Students and Educators, Scientific institutions, Companies building AI-powered applications
Industries Served
Artificial Intelligence & Machine Learning, Scientific Research (e.g., CERN, NIH, NASA), Autonomous Vehicles (e.g., Waymo), Academia & Education, Healthcare, Computer Vision & Robotics, Natural Language Processing (NLP)
Tags
Neural Networks, Keras

Keras's In-App Market Place

Does Keras have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

0

Mini Apps

Pricing Options

Free trial
Free version
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Accepted Payment Currencies

USD ($), EUR (€)

Pros & Cons

  • Simple and readable syntax ideal for beginners and pros alike
  • Multi-backend support (TensorFlow, PyTorch, JAX) increases flexibility
  • Backed by Google — ensures stability and long-term development
  • Powerful model customization with subclassing and layer design
  • Massive community and ecosystem with contributions from academia and industry
  • Advanced debugging tools less mature than native PyTorch in some areas
  • Performance tuning can require backend-specific knowledge
  • Less control over low-level tensor operations compared to raw PyTorch/JAX
  • Still dependent on backend limitations (e.g., TensorFlow graph constraints)
  • Smaller user base compared to PyTorch for research-oriented work

Keras's Support Options

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