This chapter gives an insight into Deep Learning Neural Networks and their application to Image Classification / Pattern Recognition. The principle of Convolutional Neural Networks will be described and an in-depth study of the algorithms for image classification will be made. In artificial intelligence, machine learning plays a key role. The algorithm learns when exposed to new data or environment. Object / Pattern Recognition is an integral part of machine learning and image classification is an integral part of such algorithms. The Human Visual System efficiently classifies known objects and also learns easily when exposed to new objects. This capability is being developed in Artificial Neural Networks and there are several types of such networks with increasing capabilities in solving problems. Neural networks themselves have evolved from evolutionary computing techniques that try to simulate the behavior of the human brain in reasoning, recognition and learning. Deep neural networks have powerful architectures with the capability to learn and there are training algorithms that make the networks adapt themselves in machine learning. The networks extract the features from the object and these are used for classification. The chapter concludes with a brief overview of some of the applications / case studies already published in the literature.
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