Keras | Deep Learning
Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Originally developed as a user-friendly interface to work on top of other deep learning frameworks like TensorFlow, Keras became integrated into TensorFlow starting from TensorFlow 2.0 as the official high-level API.
Keras aims to be a user-friendly, modular, and extensible deep learning library. It provides a simple and intuitive interface for creating and training neural networks, allowing developers to quickly prototype and experiment with different architectures.
Key features of Keras include:
User-friendly API: Keras offers a simple and consistent interface that makes it easy to define, configure, and train neural networks without requiring extensive knowledge of the underlying mathematical operations.
Modularity: Keras provides a building block approach to constructing neural networks, allowing developers to stack layers and define complex architectures. It offers a wide range of pre-defined layers and models, as well as the flexibility to create custom layers.
Easy customization: Keras enables users to customize neural networks by defining their own loss functions, metrics, and optimizers. It also supports the creation of custom layers, models, and callbacks.
GPU acceleration: Keras seamlessly integrates with popular deep learning libraries like TensorFlow and provides GPU acceleration for training on compatible hardware, which significantly speeds up the computation of neural networks.
Some notable companies and organizations that have adopted Keras include: Google, Netflix, Uber, Airbnb and NVIDIA.
Keras aims to be a user-friendly, modular, and extensible deep learning library. It provides a simple and intuitive interface for creating and training neural networks, allowing developers to quickly prototype and experiment with different architectures.
Key features of Keras include:
User-friendly API: Keras offers a simple and consistent interface that makes it easy to define, configure, and train neural networks without requiring extensive knowledge of the underlying mathematical operations.
Modularity: Keras provides a building block approach to constructing neural networks, allowing developers to stack layers and define complex architectures. It offers a wide range of pre-defined layers and models, as well as the flexibility to create custom layers.
Easy customization: Keras enables users to customize neural networks by defining their own loss functions, metrics, and optimizers. It also supports the creation of custom layers, models, and callbacks.
GPU acceleration: Keras seamlessly integrates with popular deep learning libraries like TensorFlow and provides GPU acceleration for training on compatible hardware, which significantly speeds up the computation of neural networks.
Some notable companies and organizations that have adopted Keras include: Google, Netflix, Uber, Airbnb and NVIDIA.
Listing Details
Main Functions
deep learning framework
Top Features
high-level interface for building and training neural networks
Typical customers
Mid size businesses, Large enterprises
Platform
Windows, MAC, Linux
Website
