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AN ANALYSIS OF CONVOLUTIONAL NEURAL NETWORKS AND THEIR APPLICATIONS

The field of machine learning concerns itself with creating models to represent phenomena in the real world based on the data that is obtained. We model the probability distributions over input and output variables in order to estimate functions which capture the relationship between the input and the output. For a statistical parameterized model, we traverse through the parameter space in order to obtain a parameterized hypothesis which best fits the data based on some optimization criterion used in the context of our model. With the increase in computing power and the availability of a large amount of data, Machine Learning has been successfully used in a wide variety of tasks. Deep learning is that subfield of machine learning which uses artificial neural networks related models to create nested representations for the data. An important part of deep learning, convolutional neural networks are computational models similar to standard ANNs but designed for data with a spatial or a grid like topology. They contain the convolution operation which is inspired from digital signal processing. Computer Vision is the field of AI which concerns itself with the task of obtaining a high level understanding from image and video data. Convolutional neural networks have been successfully used in computer vision and they have set the benchmark surpassing traditional computer vision techniques. From image classification to object detection and face recognition, CNNs have proven to be very effective. In this paper, we will understand how CNNs work and we will understand the nature of information representation in CNNs. We also look at various computer vision applications of CNNs.