More about Convolutional Neural Networks
The image processing that is an analysis of visual images is also called as Shift Invariant or Space Invariant Artificial Neural Network (SIANN) in accordance with its translational invariance properties. It finds huge consumption in areas like NLP (natural language processing), image video recognization, medical imaging and classification, etc. CNN is a powerful tool that utilizes deep learning tool to provide descriptive and generative work. The normal neural network system is not patterned to handle images, it is developed in accordance with human brain operating mechanism.
Thus, there is need to give images in reduced resolution form for processing. While in CNN it's not the case arrangement of neurons is different with a high density in front lobe which is responsible for performing visual processing in brains of human. Hence, neurons here in case of CNN are designed in a similar manner such that almost whole field of the frontal lobe is covered and this helps in eliminating problems of piecemeal images.
CNN are a prototype of multilayer perceptron is a computerized model design for replicating the ability of the human brain to recognise and discriminate. The multilayer perceptron is the interconnection of various layers of neurons to form a closed network. CNN therefore regularizes and makes up data consistent with the help of hierarchical arrangement of data and further creating different sets of data according to their type size and other factors which helps in solving complex pattern data problems.
Biologically the singleton cortex neurons respond to stimuli in specific regions called as receptive fields. The receptor fields of different layers partially overlap with each other in order to ensure they cover the entire field. Those algorithms which earlier required engineers to use their hands, now with the help of CNN and little preparations can be done easily, time-saving, in comparison to other image classification algorithm filters. These are major outbreak and revolution brought by CNN. Some features of Convolutional Neural Network (CNN) are as follows:
3D Neurones - CNN layers are three-dimensional and all neurons are connected with other neurones ensuring stacked architectural CNN structure.
Local Connectivity: Because of the presence of 3D network it has greater connectivity with adjacent and far lying layers. The principal of receptive fields and filter play a vital role in enforcing greater connectivity between the neurons and layers.
Pooling: In pooling layers of CNN the maps are broken into equal rectangular regions and each region features are brought into a single value by taking average or maximum of all values. Pooling makes CNN robust to vary ensuring data rigidity.
Integration of all these features ensures smooth functioning of CNN. Some applications of CNN are as follows:
Image Recognization and video analysis: CNN is often used in facial recognition and over the years it has increased its success rate,97.6% recognition rate of faces accurately. The video quality recognition to have reduced Less root-mean-square value.
NLP: CNN is very much efficient in solving various NLP problems and has shown outcomes in semantic parsing too. Also, NLP is a subset of a key constituent of artificial intelligence (AI), thus CNN is indirectly valuable to AI too.
Drug Protection: It helps in predicting the interaction between molecules and biological protein and hence helps in knowing the efficiency of the drug. Also helps in giving directions to modification of drug structure to get the right combination to attack the microbes.
Games: CNN is used in a variety of games like a checker, Fogell, etc. CNN competes with original players efficiently and once secured rank amongst the top 0.4%.
Convolutional Neural Network (CNN) has introduced a new and effective way of performing image recognition, data processing and such features have enabled many firms like Facebook to perform automatic work in this domain and help them grow exponentially. This is something which is making it popular and rises.