Machine Learning for Engineers
portes grátis
Machine Learning for Engineers
Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications
Neuer, Marcus J.
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
12/2024
241
Mole
9783662699942
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1 Introduction to Working with Data.- 2 Data as a Stochastic Process.- 3 Exploratory Analysis (Data Cleaning, Histograms, Principal Component Analysis, Mathematical Transformations).- 4 Fundamentals of Supervised and Unsupervised Learning Methods.- 5 Physics-Informed Learning Methods (Optimization Methods for Data Preprocessing, Integration of Transformatively-Enriched Data, Integration of Mathematical Models).- 6 Stochastic Learning Methods (Mixture-Density Networks, Credal Networks).- 7 Semantic Databases.- 8 Explainable, Trustworthy Artificial Intelligence.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Data Science;Python;Machine Learning;Reinforced Learning;Unsupervised Learning;Explainable AI;Supervised Learning;Stochastics;Learning Methods;Artificial Intelligence
1 Introduction to Working with Data.- 2 Data as a Stochastic Process.- 3 Exploratory Analysis (Data Cleaning, Histograms, Principal Component Analysis, Mathematical Transformations).- 4 Fundamentals of Supervised and Unsupervised Learning Methods.- 5 Physics-Informed Learning Methods (Optimization Methods for Data Preprocessing, Integration of Transformatively-Enriched Data, Integration of Mathematical Models).- 6 Stochastic Learning Methods (Mixture-Density Networks, Credal Networks).- 7 Semantic Databases.- 8 Explainable, Trustworthy Artificial Intelligence.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
