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. 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 to make working with image and text data easier to simplify the coding necessary for writing deep neural network code. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel.
Keras allows users to productize 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 2.4.0". 17 June 2020. Retrieved 18 June 2020.
- "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.
- "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. Retrieved 2018-11-14.
Presented by Jörg Kienitz and Nikolai Nowaczyk
The goal of this two-day workshop is to provide a detailed overview of machine learning techniques applied for finance. We offer insights into the latest techniques for modelling financial markets and focus on option pricing and calibration.
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.