aenet (ænet, The Atomic Energy Network)

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

aenet is software for atomic interaction potentials using artificial neural networks. Users can construct neural network potentials using structures of target materials and their energies obtained from first principle calculations. The generated potentials can be used to molecular dynamics or Monte Carlo simulations.

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Caffe

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source library for machine learning. Various functions on deep learning based on neural network can be used by this package. This package is especially customised for image identification, and a number of sample codes are prepared. Users can also use pre-trained models, which are open in Caffe Model Zoo. Since this package is written in C++, high-speed operation is realised.

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Chainer

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source library for machine learning. Various functions on machine learning/deep learning are implemented in this package. Using flexible user-friendly description, various types of networks from simple to complex ones can be implemented. GPGPU parallel computation based on CUDA is also supported.

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Keras

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source numerical library for machine learning. Using other machine learning numerical libraries (TensorFlow, CNTK, Theano, etc.), users can construct neural networks by relatively short codes. Since a number of methods in machine learning and deep learning are implemented, users can try state-of-the-art methods easily. This package is written by Python.

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NetKet

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. Users can perform machine learning algorithms to find the ground-state of many-body Hamiltonians such as supervised learning of a given state and optimization of neural network states by using the variational Monte Carlo method.

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PyTorch

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An interface package to use Torch (the open-source numerical library for machine learning) from Python. Users can easily implement deep learning based on neural networks, and can use various state-of-the-art methods. This package supports GPGPU parallel computation, and realises high-speed operation. A front-end interface for C++ is also prepared.

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scikit-learn

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.

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TensorFlow

  • Level of openness 2 ★★☆
  • Document quality 3 ★★★

A numerical library for machine learning. Various functions on machine learning (including supervised learning and unsupervised learning) are implemented in this package. Complex network can be expressed in a simple form by using data flow graphs. Efficient CPU/GPGPU parallel computation is supported to realise efficient operation on large scale data.

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Theano

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source numerical library for machine learning. Various functions related to deep learning are implemented. This package directly treats equations as such, and have useful routines such as matrix operation and auto partial derivative. Users can convert their codes into C language, and can compile it. High speed operation by GPGPU parallel calculation is supported. A number of tutorials are available.

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Torch

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source numerical library for machine learning. Various functions related to deep learning based on neural networks are implemented. Users can implement complex network with flexible description, and can try various state-of-the-art methods. This package is used in a number of companies in the world. This package is written by the script language, lua.

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