Deep Learning in Physics

PART I: BASIC UNDERSTANDING Relevance of Machine Learning Basic Idea of Deep Learning Neural Networks as Multivariate, Multidimensional Models Optimization of Network Parameters - Quality of Modelling PART II: NETWORK ARCHITECTURES Basic Architecture and Extensions Analysis of Image Data Analysis of Point Clouds Time Series and Variable Input Data Learning with Success Targets Energy-Driven Learning Methods Reduction to Essential Information Cooperation of Several Networks PART III: NETWORK INSIGHTS AND ADVANCED CONCEPTS Understanding of Trained Networks Systematic Uncertainties Weak and Unsupervised Training Hybrid Architectures

Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.

• Inteligência artificial
• Química