Deep learning is a subcategory of machine learning in the field of artificial intelligence. Thus Artificial intelligence is a trending topic in the field of computing. Recent advancements in Artificial Intelligence, deep learning, computing resources and availability of large training datasets made tasks such as computer vision and natural language processing extremely fast and accurate. With rigorous training on the high end and graphics enabled machine for several months continuously the data and information gathered have been compiled in this research paper with all the obligatory information required to comprehend Convolutional Neural Networks.Ī scientific study on the importance of machine learning and its applications in the field of computer vision is carried out in this paper. This research aims to make the process of understanding different neural networks and working with them easy. The performance of these neural networks is evaluated and benchmarked using well known and most commonly used Ciphar10 and Ciphar100 datasets.
These 5 CNNs are LeNet, AlexNet, VGGNet16, ResNet50, and GoogLeNet. This paper contains a detailed study and data-rich analysis of the 5 most popular Convolutional Neural networks (CNNs) for Image Detection and Identification. In today's fast and furious world where new techniques and models are being developed every day for the sake of increasing the efficiency and performance of the neural network, this research conducts an in-depth study about some of the most popular and important Convolutional neural network models. We have tried to put an honest effort in this paper to analyze the Convolutional Neural Network (CNN) and the various developments made in its area of research. The major applications of convolutional neural network include image recognition, natural language processing, recommender systems and video recognition. The main motivation behind the development of convolutional networks is the biological processes and CNNs are considered as the multilayer perceptrons' variations that are designed for the purpose of providing the minimal usage of pre-processing. A receptive field is a restricted part of the space where a respond to stimuli are done by the individual cortical neurons.
It uses the idea of animal's visual cortex organization to achieve connectivity pattern between its neurons. Convolutional Neural Network (CNN) is the most important Deep Neural Network (DNN) architecture to implement the Deep Learning's application of data and pattern representation in an effective and efficient manner.
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