This post provides the details of the architecture of _Convolutional Neural Network _(CNN), functions and training of each layer, ending with a summary of the training of CNN.

1. The basic CNN architecture consists of: Input->(Conv+ReLU)->Pool->(Conv+ReLU)->Pool-> Flatten->Fully Connected->Softmax->Output
2. The feature extraction is carried out in the Convolutional layer+ReLU and Pooling layers and the classification is carried out in Fully Connected and Softmax layers.

3. First Convolutional Layer:

• The primary purpose of this layer is to extract features from the input image.
• The convolutionis used to extract features because it preserves the spatial relationship between pixels by learning image features by using small squares of input data.
• The convolutional layer have the following attributes:-

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