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Chainer

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Theano

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Theano vs Chainer: What are the differences?

What is Theano? Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C impleme.

What is Chainer? A Powerful, Flexible, and Intuitive Framework for Neural Networks. It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

Theano and Chainer can be primarily classified as "Machine Learning" tools.

Theano and Chainer are both open source tools. Theano with 8.88K GitHub stars and 2.5K forks on GitHub appears to be more popular than Chainer with 4.98K GitHub stars and 1.32K GitHub forks.

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What is Chainer?

It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

What is Theano?

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

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What companies use Chainer?
What companies use Theano?
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What tools integrate with Chainer?
What tools integrate with Theano?
What are some alternatives to Chainer and Theano?
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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.
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.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Caffe
It is a deep learning framework made with expression, speed, and modularity in mind.
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