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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.
Keras is a tool in the Machine Learning Tools category of a tech stack.
Keras is an open source tool with 50.8K GitHub stars and 18.7K GitHub forks. Here’s a link to Keras's open source repository on GitHub

Who uses Keras?

120 companies reportedly use Keras in their tech stacks, including Delivery Hero, Hepsiburada, and Ruangguru.

691 developers on StackShare have stated that they use Keras.

Keras Integrations

Python, TensorFlow, scikit-learn, Databricks, and Streamlit are some of the popular tools that integrate with Keras. Here's a list of all 18 tools that integrate with Keras.
Pros of Keras
Quality Documentation
Easy and fast NN prototyping
Supports Tensorflow and Theano backends
Decisions about Keras

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

I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

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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 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 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.
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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.
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Keras's Followers
852 developers follow Keras to keep up with related blogs and decisions.