Titlebook: A Guide to Convolutional Neural Networks for Computer Vision; Salman Khan,Hossein Rahmani,Mohammed Bennamoun Book 2018 Springer Nature Swi

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https://doi.org/10.1007/978-3-663-19643-3d their historical background. Neural networks are inspired by the working of cerebral cortex in mammals. It is important to note, however, that these models do not closely resemble the working, scale and complexity of the human brain. Artificial neural network models can be understood as a set of b

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Ternary Leibniz Color Algebras and Beyond,re required to be tuned appropriately for a given computer vision task (e.g., image classification and object detection). In this chapter, we will discuss various mechanisms and techniques that are used to set the weights in deep neural networks. We will first cover concepts such as weight initializ

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Ternary Leibniz Color Algebras and Beyond,ts. It is an active research field for convolutional neural network applications. The most popular of these applications include, classification, segmentation, detection, and scene understanding. Most CNN architectures have been used for computer vision problems including, supervised or unsupervised

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,Über die Theorie der algebraischen Formen,versity of Montreal) and industry groups (e.g., Google, Facebook, Microsoft) to develop deep learning frameworks. It is mainly due to their popularity in many applications domains over the last few years. The key motivation for developing these libraries is to provide an efficient and friendly devel

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,Über die Theorie der algebraischen Formen,fficient, and flexible vision systems. This book aimed to introduce different aspects of CNNs in computer vision problems. The first part of this book (Chapter 1 and Chapter 2) introduced computer vision and machine learning subjects, and reviewed the traditional feature representation and classific

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