Inhaltsangabe
This textbook offers chemists insights into the much discussed - and often not fully understood - concept of neural networks. It starts by describing the fundamental principles, and pinpoints the five most widely used neural networks and learning strategies, illustrating them with lucid examples. The second part of the book helps chemists to get a grip on neural networks by showing them numerous applications from diverse fields. These include analytical chemistry and spectroscopy; process control and optimization of product composition; reactivity of organic compounds and QSAR; maps of electrostatic potentials; and secondary structures of proteins. This self-study guide leads both students and professionals from introductory principles to practical application. It aims to enable readers to apply neural networks to their problems, either with a commercial neural network package or with a self-made programme.
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This textbook offers chemists insights into the much discussed - and often not fully understood - concept of neural networks. It starts by describing the fundamental principles, and pinpoints the five most widely used neural networks and learning strategies, illustrating them with lucid examples. The second part of the book helps chemists to get a grip on neural networks by showing them numerous applications from diverse fields. These include analytical chemistry and spectroscopy; process control and optimization of product composition; reactivity of organic compounds and QSAR; maps of electrostatic potentials; and secondary structures of proteins. This self-study guide leads both students and professionals from introductory principles to practical application. It aims to enable readers to apply neural networks to their problems, either with a commercial neural network package or with a self-made programme.
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