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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Chainer vs Theano

Chainer vs Theano

OverviewComparisonAlternatives

Overview

Theano
Theano
Stacks32
Followers65
Votes0
GitHub Stars10.0K
Forks2.5K
Chainer
Chainer
Stacks17
Followers23
Votes0
GitHub Stars5.9K
Forks1.4K

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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Detailed Comparison

Theano
Theano
Chainer
Chainer

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

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.

-
Supports CUDA computation;Runs on multiple GPUs ;Supports various network architectures ;Supports per-batch architectures
Statistics
GitHub Stars
10.0K
GitHub Stars
5.9K
GitHub Forks
2.5K
GitHub Forks
1.4K
Stacks
32
Stacks
17
Followers
65
Followers
23
Votes
0
Votes
0
Integrations
NumPy
NumPy
Python
Python
Python
Python
NumPy
NumPy
CUDA
CUDA

What are some alternatives to Theano, Chainer?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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