CONVOLUTIONAL NEURAL NETWORKS

Lakshmi
5th Sem BCA, NCMS

One of the most popular neural networks is the convolutional neural networks. In recent years it has given breaking results in different fields related to pattern recognition, image processing, voice recognition etc. The most beneficial aspect of CNNs is reducing the parameters in artificial neural networks. This achievement prompted researcher's and developers to solve the complex problems. In deep learning a convolutional neural network is a class of Deep neural networks it is most commonly applied to analyzing visual imagery. The term "convolution" is a mathematical operation on two functions that produces third function. CNNs are regularized versions of multilayer perceptron , multilayer perceptron  usually mean fully connected networks that is each neuron  in one layer  is connected to all neurons in the next layer. CNNs are inspired by biological processes in that the connectivity pattern between neurons. The architecture of the CNNs is different from the traditional multilayer perceptron  . This is to guarantee a certain degree of shift and distortion invariance . To do so, three design ideas are merged, which are, local receptive fields, common weights, and spatial and temporal subsampling. The CNNs consists of an input and output layers as well as multiple hidden layers. The hidden layers are convolutional layer, pooling layer, fully connected layer, receptive field layer, weights.  A typical architecture consists of several convolution layers and pooling layers followed by one or more fully connected layers.

The convolution function

The applications of convolutional neural networks are

Computer vision

Scene labelling     

Natural language processing                                                                                                        

Face Recognition      

Action Recognition                                                                                           

Text and image classification                                                                                                                       

Speech recognition

Convolutional neural networks have accomplished astonishing achievements across various domains including medical research, and an increasing interest has emerged in radiology. Therefore CNN offers better accuracy when compared with other standard approaches .