Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. As of version 2.4, only TensorFlow is supported. However, starting with version 3.0 (including its preview version, Keras Core), Keras will become multi-backend again, supporting TensorFlow, JAX, and PyTorch. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author and maintainer is François Chollet, a Google engineer. Chollet is also the author of the Xception deep neural network model.
Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools for working with image and text data to simplify programming in deep neural network area. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel.
Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU).
- "Release 2.14.0". 12 September 2023. Retrieved 18 September 2023.
- "Keras backends". keras.io. Retrieved 2018-02-23.
- "Why use Keras?". keras.io. Retrieved 2020-03-22.
- "R interface to Keras". keras.rstudio.com. Retrieved 2020-03-22.
- "Introducing Keras Core: Keras for TensorFlow, JAX, and PyTorch". Keras.io. Retrieved 11 July 2023.
- "Keras Documentation". keras.io. Retrieved 2016-09-18.
- Chollet, François (2016). "Xception: Deep Learning with Depthwise Separable Convolutions". arXiv:1610.02357.
- "Core - Keras Documentation". keras.io. Retrieved 2018-11-14.
- "Using TPUs | TensorFlow". TensorFlow. Archived from the original on 2019-06-04. Retrieved 2018-11-14.
Mastering Python for Finance:
Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
James Ma Weiming -
Explore advanced financial models, build state-of-the-art infrastructure, empower your financial applications.