Ergebnisse für: Deep Neural Networks
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This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testin...
This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer ...
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs ...
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in...
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in...
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs ...
This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradie...
This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradie...
This book shows you how to build real-time image processing systems all the way through to house automation. Find out how you can develop a system based on small 32-bit ARM processors that gives you complete control through voice commands.Real-time image processing systems are utilized in a wide var...
This book shows you how to build real-time image processing systems all the way through to house automation. Find out how you can develop a system based on small 32-bit ARM processors that gives you complete control through voice commands.Real-time image processing systems are utilized in a wide var...
This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer ...
This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer ...
Today, a large number of applications depend on deep neural networks (DNN) to process data and
perform complicated tasks at restricted power and latency specifications. Therefore, processing-in-
memory (PIM) platforms are actively explored as a promising approach to improve DNN computing
systems�...
This dissertation examines the evaluation of high-definition maps using LiDAR point clouds as sensor data and deep neural networks (DNNs). The developed methods are suitable for both automated driving and geodesy. By utilizing the map as an additional input, the DNN can verify the map even under adv...
This dissertation examines the evaluation of high-definition maps using LiDAR point clouds as sensor data and deep neural networks (DNNs). The developed methods are suitable for both automated driving and geodesy. By utilizing the map as an additional input, the DNN can verify the map even under adv...
This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testin...
This book looks at how quantum computing, neural networks, and next-generation communication systems can work together in new and exciting ways. The book presents details about cutting edge techniques, such as Tensor Networks and the n-Sci framework, that make 6G and 7G networks faster, safer, and a...
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concep...
This book looks at how quantum computing, neural networks, and next-generation communication systems can work together in new and exciting ways. The book presents details about cutting edge techniques, such as Tensor Networks and the n-Sci framework, that make 6G and 7G networks faster, safer, and a...
A central task in machine learning, computer vision, and signal processing is to extract characteristic features of signals. Feature extractors based on deep convolutional neural networks have been applied with significant success in a wide range of practical machine learning tasks such as classific...