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CNN typically uses images as data. How? The image is expressed by Vertex(Node) and Edge. graph Composed of Vertex(Node), Edge. In other words, set of Vertex(Node)and Edge. Vertex(Node) It is the fundamental unit of which graphs are formed. Featureless and indivisible objects. Edge(Arc) It represented by a line or arrow extending from one vertex to another. Relationship between vertexes. Sparse G..

LeNet-5 It is one of the very first convolutional neural networks (CNNs). 'Le' means 'the' in French language, hence LeNet means 'the network'. Every convolutional layer includes three parts: convolution, pooling, and nonlinear activation functions Using convolution to extract spatial features (Convolution was called receptive fields originally) The average pooling layer is used for subsampling...

ResNet A 152-layer CNN architecture, which creates The Residual Blocks idea to address the issue of the vanishing/exploding gradient. Residual Block Plane layer y=f(x) - Not add the input x - y is Feature vector(Feature Map). - y is the information newly learned through x. - y is not preserve information when generate new information. - The deeper the layers, too much mapping to learn at once. R..

AlexNet - It consists of 5 convolution layers, 3 max-pooling layers, 2 Normalized layers, 2 fully connected layers and 1 SoftMax layer. - Each convolution layer consists of a convolution filter and a non-linear activation function called “ReLU”. - The pooling layers are used to perform the max-pooling function - and the input size is fixed due to the presence of fully connected layers. The input..

VGG16 Visual Geometry Group 16 Layers deep. A convolutional neural network that is 16 layers deep. Initially, VGG is a model designed to study how the depth of neural networks affects. So, the filter size of the convolution layer was fixed at the smallest 3x3. This is because if the filter size is large, the image size quickly becomes small and it is difficult to make the depth. Models before VG..