Deep Learning Approach to Real-World Application: An Empirical Analysis

Recent advances in deep learning (DL) support the modernization of several intellectual systems and applications, such as image recognition. DL, which is a subset of machine learning (ML) techniques, may uncover very large datasets perceptually and professionally. DL methods based on artificial neural networks (ANN) have become a hot topic in the world of computing because they can learn skills from data and are used in many software areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. Still, making a good DL model is an interesting task because it is active and because the problems and data in the real world are different. In order to make it easy for non-experts and innocent consultants to make the best choice, we set up an environment to test the effectiveness of different deep learning approaches, such as CNN, RNN, MLP, and others, in order to fully understand how their algorithms and methods work. The DOG image dataset, which contains images of 120 different dog breeds, is used for this purpose. After 10, 20, 30, and 40 epochs, the performance of various deep learning architectures (ResNET50V2 architecture for CNN, imageRNN architecture for RNN, and ResMLP architecture for the MLP algorithm) is examined. CNN has observed precisions of 74.71, 82.04, 81.49, 83.89, and 92.45; RNN has observed precisions of 92.49, 86.91, 82.24, 78.03, and 69.18; and MLP has observed precisions of 92.33, 89.42, 84.53, 75.49, and 74.36. CNN’s architecture may also be highly helpful for image datasets.