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Ergebnis der Suche nach: tit all "Neural Networks and Deep Learning"
im Bestand: Gesamter Bestand

1 - 10 von 44

	1	Deep learning-based prediction of particle size distributions in construction and demolition waste recycling using convolutional neural networks on 3D laser triangulation data Kroell, Nils. - Aachen : Universitätsbibliothek der RWTH Aachen, 2025
	2	Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications Enthalten in Nanotechnology reviews Bd. 14, 2025, Nr. 1. 24 S.
	3	Physical Artificial Neural Networks - Optical and Atomic Approaches to Deep Learning Hansmann, Kai Niklas. - Darmstadt : Universitäts- und Landesbibliothek, 2024
	4	Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning Heinrich, Kai. - Würzburg : Universität Würzburg, 2024
	5	A wholistic view of continual learning with deep neural networks: forgotten lessons and the bridge to active and open world learning Mundt, Martin. - Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2023
	6	Geometric deep learning and equivariant neural networks Enthalten in Artificial intelligence review 4.6.2023: 1-58
	7	Deep neural networks and machine learning radiomics modelling for prediction of relapse in mantle cell lymphoma Lisson, Catharina Silvia. - Ulm : Universität Ulm, 2022
	8	Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research Bodesheim, Paul. - Jena : Friedrich-Schiller-Universität Jena, 2022
	9	Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study Schilling, Maximilian. - Aachen : Universitätsbibliothek der RWTH Aachen, 2022
	10	Detecting and responding to hostile disinformation activities on social media using machine learning and deep neural networks Enthalten in Neural computing & applications 9.6.2022: 1-23