Keras is a powerful and user-friendly deep learning framework designed to enable fast experimentation with neural networks. It is an open-source software library that provides a Python interface for artificial neural networks, acting as an interface for the TensorFlow library. Keras is a high-level neuralnetworks API, written in Python and capable of running on top of TensorFlow, JAX, or PyTorch. It allows for easy and fast prototyping, supports both convolutional networks and recurrent networks, and runs seamlessly on both CPUs and GPUs.
Keras is a user-friendly framework that offers flexibility and user experience. It supports multiple backends, allowing developers to focus on research. Its integration with TensorFlow, JAX, and PyTorch allows for easy model transfer across different environments.
Seller
Keras
HQ Location
Mountain View, California,USA
Company Website
https://keras.io/
Year Founded
2015
Built-in Training Features
Preprocessing Layers
Support for Convolutional and Recurrent Networks
Integration with TensorFlow
Comprehensive Documentation
Fast Experimentation
Extensibility
Cross-Platform Compatibility
custom
Per User Per Month
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Where in GCC does Keras have offices?
Not available.
Who are Keras customers in the Middle East?
Not available.
What is Kera's local address?
Not available.
Is the Keras platform available in Arabic?
No.
Does the Keras platform use AI? And where.
Yes, Keras is fundamentally designed for building and training deep learning models, which are a subset of artificial intelligence (AI). Here are some key areas where Keras utilizes AI:
Model Building and Training: Keras provides a high-level API for creating and training neural networks. This includes various types of models, such as sequential models and functional API models, which can be used for tasks like image recognition, natural language processing, and more.
Prediction and Inference: Once models are trained, Keras can be used to make predictions on new data. This is often done in real-time applications such as recommendation systems, fraud detection, and autonomous driving.
Deployment: Keras models can be deployed on various platforms, including cloud services like Google Cloud's AI Platform, which allows for scalable and efficient serving of AI models.
Research and Experimentation: Keras is widely used in academic and industrial research to experiment with new AI algorithms and techniques. Its user-friendly interface makes it accessible for rapid prototyping and testing.
Is Keras a Web 3 company?
No.
Are there any Web 3 components?
No.
Keras
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