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Deep Learning library for Theano and TensorFlow
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What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Keras is a tool in the Machine Learning Tools category of a tech stack.
Keras is an open source tool with 43.6K GitHub stars and 16.6K GitHub forks. Here’s a link to Keras's open source repository on GitHub

Who uses Keras?

Companies
71 companies reportedly use Keras in their tech stacks, including StyleShare Inc., Home61, and Suggestic.

Developers
257 developers on StackShare have stated that they use Keras.

Keras Integrations

Python, TensorFlow, scikit-learn, Polyaxon, and Lobe are some of the popular tools that integrate with Keras. Here's a list of all 8 tools that integrate with Keras.

Why developers like Keras?

Here’s a list of reasons why companies and developers use Keras
Keras Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Keras in their tech stack.

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 6 upvotes · 160.5K views
atUber TechnologiesUber Technologies
PyTorch
Keras
TensorFlow

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

Keras's Features

  • neural networks API
  • Allows for easy and fast prototyping
  • Convolutional networks support
  • Recurent networks support
  • Runs on GPU

Keras Alternatives & Comparisons

What are some alternatives to Keras?
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
ML Kit
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
See all alternatives

Keras's Stats

Keras's Followers
253 developers follow Keras to keep up with related blogs and decisions.
Daniel Rusznyak
Jacob Kim
Sourav Halder
Xing Sun
Sam Chaher
관영 오
NawazDalwai
Kaan Taha Köken
Bruno Morais Neves de Castro
Issey Masuda Mora