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Table 2 | Publicly accessible learning resources and tools related to machine learning
Name Description URL
General-purpose machine-learning frameworks
Caret Package for machine learning in R https://topepo.github.io/caret
Deeplearning4j Distributed deep learning for Java https://deeplearning4j.org
H2O.ai Machine-learning platform written in Java that can be imported as a Python or R library https://h2o.ai
Keras High-level neural-network API written in Python https://keras.io
Mlpack Scalable machine-learning library written in C++ https://mlpack.org
Scikit-learn Machine-learning and data-mining member of the scikit family of toolboxes built around the
SciPy Python library
http://scikit-learn.org
Weka Collection of machine-learning algorithms and tasks written in Java https://cs.waikato.ac.nz/ml/weka
Machine-learning tools for molecules and materials
Amp Package to facilitate machine learning for atomistic calculations https://bitbucket.org/andrewpeterson/amp
ANI Neural-network potentials for organic molecules with Python interface https://github.com/isayev/ASE_ANI
COMBO Python library with emphasis on scalability and eciency https://github.com/tsudalab/combo
DeepChem Python library for deep learning of chemical systems https://deepchem.io
GAP Gaussian approximation potentials http://libatoms.org/Home/Software
MatMiner Python library for assisting machine learning in materials science https://hackingmaterials.github.io/matminer
NOMAD Collection of tools to explore correlations in materials datasets https://analytics-toolkit.nomad-coe.eu
PROPhet Code to integrate machine-learning techniques with quantum-chemistry approaches https://github.com/biklooost/PROPhet
TensorMol Neural-network chemistry package https://github.com/jparkhill/TensorMol
(PDF) Machine learning for molecular and materials science. Available from: https://www.researchgate.net/publication/326608140_Machine_learning_for_molecular_and_materials_science [accessed Dec 06 2018].
Publicly accessible learning resources and tools related to machine learning
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原文地址：https://www.cnblogs.com/tanrong/p/10079687.html
