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Machine learning in chemistry / Jon Paul Janet & Heather J. Kulik.
- Format:
- Book
- Author/Creator:
- Janet, Jon Paul, Massachusetts Institute of Technology., author.
- Kulik, Heather J., Massachusetts Institute of Technology., author.
- Series:
- ACS in focus. 2691-8307
- ACS in focus, 2691-8307
- Language:
- English
- Subjects (All):
- Machine learning.
- Chemistry--Computer programs.
- Chemistry.
- Supervised learning (Machine learning).
- Chemistry--Computer simulation.
- Machine theory.
- Artificial intelligence.
- Linear models (Statistics).
- Kernel functions--Computer programs.
- Kernel functions.
- Trees (Graph theory)--Computer programs.
- Trees (Graph theory).
- Chemistry--Molecular aspects--Computer programs.
- Neural networks (Computer science).
- Computational Chemistry.
- Machine Learning.
- Supervised Machine Learning.
- Computer Simulation.
- Artificial Intelligence.
- Linear Models.
- Neural Networks, Computer.
- Computer programs.
- Medical Subjects:
- Computational Chemistry.
- Machine Learning.
- Supervised Machine Learning.
- Computer Simulation.
- Artificial Intelligence.
- Linear Models.
- Neural Networks, Computer.
- Genre:
- Conference papers and proceedings.
- Physical Description:
- 1 online resource : illustrations (some color).
- polychrome
- Place of Publication:
- Washington, DC, USA : American Chemical Society, 2020.
- System Details:
- text file
- Contents:
- Advancing Research through Machine Learning
- Supervised Machine Learning for the Chemical Sciences
- Linear Models, Kernels, and Trees
- Representations of Atomistic Systems
- Neural Networks and Learned Representations
- Applying Machine Learning Models in Chemistry.
- Notes:
- Includes bibliographical references and index.
- Description based on publisher-supplied information and home-page.
- Local Notes:
- American Chemical Society, ACS In Focus eBooks - 2020 Front Files.
- ISBN:
- 9780841299009
- Access Restriction:
- Restricted for use by site license.
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