Convolution Neural Networks (CNN), known as ConvNets are widely used in many
visual imagery application, object classification, speech recognition. After
the implementation and demonstration of the deep convolution neural network in
Imagenet classification in 2012 by krizhevsky, the architecture of deep
Convolution Neural Network is attracted many researchers. This has led to the
major development in Deep learning frameworks such as Tensorflow, caffe, keras,
theno. Though the implementation of deep learning is quite possible by
employing deep learning frameworks, mathematical theory and concepts are harder
to understand for new learners and practitioners. This article is intended to
provide an overview of ConvNets architecture and to explain the mathematical
theory behind it including activation function, loss function, feedforward and
backward propagation. In this article, grey scale image is taken as input
information image, ReLU and Sigmoid activation function are considered for
developing the architecture and cross-entropy loss function are used for
computing the difference between predicted value and actual value. The
architecture is developed in such a way that it can contain one convolution
layer, one pooling layer, and multiple dense layers