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What is Keras?
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Use Keras if you need a deep learning library that:
Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
Supports both convolutional networks and recurrent networks, as well as combinations of the two.
Runs seamlessly on CPU and GPU.
Read the documentation at Keras.io.
Why use Keras?
There are countless deep learning frameworks available today. Why use Keras rather than any other? Here are some of the areas in which Keras compares favorably to existing alternatives.
Keras prioritizes developer experience
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster -- which in turn helps you win machine learning competitions.
This ease of use does not come at the cost of reduced flexibility: because Keras integrates with lower-level deep learning languages (in particular TensorFlow), it enables you to implement anything you could have built in the base language. In particular, as tf.keras, the Keras API integrates seamlessly with your TensorFlow workflows.
Keras makes it easy to turn models into products
Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework:
On iOS, via Apple’s CoreML (Keras support officially provided by Apple). Here's a tutorial.
On Android, via the TensorFlow Android runtime. Example: Not Hotdog app.In the browser, via GPU-accelerated JavaScript runtimes such as Keras.js and WebDNN.
On Google Cloud, via TensorFlow-Serving.In a Python webapp backend (such as a Flask app).
On the JVM, via DL4J model import provided by SkyMind.
On Raspberry Pi.
Keras development is backed by key companies in the deep learning ecosystem
Keras development is backed primarily by Google, and the Keras API comes packaged in TensorFlow as tf.keras. Additionally, Microsoft maintains the CNTK Keras backend. Amazon AWS is maintaining the Keras fork with MXNet support. Other contributing companies include NVIDIA, Uber, and Apple (with CoreML).
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