Understanding Convolutional Neural Networks

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Convolutional neural network: a review of models, methodologies and applications to object detection (2019, methodology): “RBM can be employed as a part of deep learning models, such as convolutional neural systems [15], pre-preparing and DBN [16].”
Understanding of a convolutional neural network (2017, background): “color image of a CIFAR-10 dataset with a width and height of 32×32 pixels, and a depth of 3 which RGB channel) or a video (gray scale video whose height and width are the resolution, and the depth are the frames) or even an experimental video, which has width and height of (L × L) sensor values, and”
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017, background): “Finally, some of the DCNN theoretical considerations, many of which are concisely summarized by Koushik (2016), are introduced in section 6.1.”
Deep Learning for Time-Series Analysis (2017, methodology): “Additionally, we refer the reader to [19] for applications of RNN as well as more details on the implementation of a LSTM, and to [54] for details on CNN. AnANN is basically a network of computing units linked by directed connections.”