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A Primer about Machine Learning in Catalysis – A Tutorial with Code
Author(s) -
Palkovits Stefan
Publication year - 2020
Publication title -
chemcatchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.497
H-Index - 106
eISSN - 1867-3899
pISSN - 1867-3880
DOI - 10.1002/cctc.202000234
Subject(s) - python (programming language) , computer science , workflow , artificial intelligence , hyperparameter , code (set theory) , programming language , artificial neural network , machine learning , database , set (abstract data type)
Based on a well‐edited dataset from literature by Schmack et al. [1] this manuscript provides a tutorial‐like introduction to Machine Learning (ML) and Data Science (DS) based on the actual programming code in the Python programming language. The study will not only try to illustrate a ML workflow, but will also show important tasks like hyperparameter tuning and data pre‐processing which often cover much of the time of an actual study. Moreover, the study spans from classical ML methods to Deep Learning with Neural Networks.