Feed Forward and Backward Run in Deep Convolution Neural Network
This is an incremental educational resource aimed at helping new learners and practitioners understand the mathematical concepts of deep learning, which are often hard to grasp despite the availability of frameworks.
The article provides an overview of ConvNet architecture and explains the mathematical theory behind it, including activation functions, loss functions, and feedforward and backward propagation, using a simple architecture with one convolution layer, one pooling layer, and multiple dense layers.
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