An Approach to Recognize Efficient Deep Learning Model for Pattern Recognition

Pattern recognition using deep learning is one of the earliest and prevailing computer vision tasks and is a frequently used research area in today's world. Deep Learning methods are currently being used in numerous applications such as Video and speech recognition, Computer Vision tasks, cybernetics, text mining, financial fraud detection, etc. DL is derived from classical neural networks which improvises them extensively. Recently developed DL algorithms have achieved excellent performance in various applications mentioned above. Despite the successful use of deep learning models in numerous domains, constructing a suitable deep learning model remains a difficult job. This difficulty arises from the dynamic and diverse nature of real-world issues and inputs. This paper emphasizes recent developments in deep learning, types of DL techniques, its applications and further highlights the challenges and findings to ease researchers in identifying research needs in the area of pattern recognition.