Abstract
In artificial intelligence, the area is going rapidly towards tackling and solving problems that are intellectually challenging for human beings, its almost straightforward for machines. A list of formal and analytical rules creates the problem. The computer gains experience automatically by executing the same problem again and again by repeating the ideas by defining the relationship between the concepts. There are many architectures to enhance the system to perform accurately and efficiently. The architecture helps to classify and extract the multiple unique features using many stages from the source data. This innovative CNN architecture reduces the complex problem by breaking into simple concepts, and then it is fed into hidden layers of the architecture. Further, it concentrates on loss function, structural reformulation, optimization, weight sharing, parameter regularization and generalization. Thus, the computer learns more complexity about the concepts on its own, and it works more accurately and efficiently.
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Bhargavi, G., Vaijayanthi, S., Arunnehru, J., Reddy, P.R.D. (2021). A Survey on Recent Deep Learning Architectures. In: Manoharan, K.G., Nehru, J.A., Balasubramanian, S. (eds) Artificial Intelligence and IoT. Studies in Big Data, vol 85. Springer, Singapore. https://doi.org/10.1007/978-981-33-6400-4_5
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