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What is a Convolutional Neural Network ?
A Convolutional Neural Network is a network of biological neurons, or, in a modern sense, an artificial Convolutional Neural Network, composed of artificial neurons or nodes. Standard definition of convolutional neural network is a feed forward neural network that is generally used to analyze visual images by processing data with grid topology. Neural networks are the part of machine learning and it is the heart of the deep learning algorithm. It contains node layers, containing an input layer, one or more hidden layers, and an output layer. Each node is connected to each other and has associated weight and threshold. Convolutional neural network (CNN) is a deep learning algorithm which takes input image assigns importance to various aspects/objects in the image and is able to differentiate one from the other The preprocessing required in a CNN is much lower as compared to other classification algorithms.
Why Convolutional Neural Network ?
In Convolutional neural networks, neurons layers will be connected with a small region of the layer. Unlike the fully connected networks, neurons will be connected with all neurons of the previous layer. Because of this we need to handle less amount of weight and we need less number of neurons.
How do work a convolutional neural network?
A CNN is designed to mimic the connectivity pattern of neurons within the human brain. The neurons within a CNN are split into a three-dimensional structure, with each set of neurons analyzing a small region or feature of the input. In other words, each group of neurons specializes in identifying one part of the image. CNNs use the predictions from the layers to produce a final output that presents a vector of probability scores to represent the likelihood that a specific feature belongs to a certain class.
Following is the CNN architecture diagram of recognizing handwritten digits.
There main types of layers in convolutional neural networks.
Convolutional neural networks Layer
Fully-connected networks (FC) Layer
Convolutional Neural Networks
The Convolutional layer is the core building block of a Convolutional Network that does most of the computational heavy lifting. In this layer the number of filters that perform convolution operation. Every image is considered as a matrix of pixel values. This is the first layer of CNN where we convolve the image or data in general using filters or kernels. Filters are small units that we apply across the data through a sliding method. Filter is the same as the input. Convolutional Neural Network operation involves taking the element-wise product of filters in the image and then evaluating those values for every sliding action.
Example image of Convolutional Neural Network Layers
The next layer is the activation layer. Activation layer only nonlinear activation functions are used between subsequent activation convolutional layers. Typical artificial neural network ten layers of ANN without activation function is effective as just having a single layer.
Pooling layer reduces the spatial size of the Convolved Feature. Pooling layer reduces the spatial size of the Convolved Feature. It is down sampling of features. There are two hyper parameters introduced in the pooling layer; first is the dimension of the spatial extent and second is the stride which is how many features the sliding window skips along the width and height. Common pooling layer 2*2 max filter with stride of two. This is a non overlapping filter. Max filter returns the maximum value among the features in the region. Average filter returns the average feature in the region. Max pooling layer works better than this. Pooling is applied through every layer in the 3 d volume depth of the feature map after pooling will remain unchanged. Pooling layer reduces the chances of overfitting as there are less parameters.
Fully-connected networks : Applies weights over the input generated by the feature analysis to predict an accurate label. Output of the convolutional Neural Network layer represents the high level features in data. Convolutional layers provide meaningful low dimensional, invariant feature space and in fully connected layer non linear function. Pooling layer output is feature map and fully connected layer output is 1D feature vector.
Project Based on Convolution neural network
Real time gender detection model
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