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General Process of Convolutional Neural Network The height of the convolutional filter and the number of filter banks determines the dimension of the output vector in layer of a convolutional. Max-pooling is referred as the resulting vector after the apply of convolutional filter with presence or absence of overlap on selecting the maximum value from N adjacent elements. Distortion is minimized as selecting the largest value from neighbour filter banks. 2-dimensional output is resulted without overlap and the pooling size is 3.

General Process of Convolutional Neural Network The height of the convolutional filter and the number of filter banks determines the dimension of the output vector in layer of a convolutional. Max-pooling is referred as the resulting vector after the apply of convolutional filter with presence or absence of overlap on selecting the maximum value from N adjacent elements. Distortion is minimized as selecting the largest value from neighbour filter banks. 2-dimensional output is resulted without overlap and the pooling size is 3.

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Fig. 2: A Fully Connected Feed forward with One Hidden Layer and One...
Fig. 3: General structure of Multilayer Perceptron On obtaining epochs...
Fig. 5: General Process of Convolutional Neural Network The height of...
Fig. 6: LSTM Memory Unit
Fig. 8: Precision Comparison with Different Methods
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