Abstract
Deep learning is a rapidly developing area in data science research. Deep learning is basically a mix of machine learning and artificial intelligence. It proved to be more versatile, inspired by brain neurons, and creates more accurate models compared to machine learning. Yet, due to many aspects, making theoretical designs and conducting necessary experiments are quite difficult. Deep learning methods play an important role in automated systems of perception, falling within the framework of artificial intelligence. Deep learning techniques are used in IOT applications such as smart cities, image recognition, object detection, text recognition, bioinformatics, and pattern recognition. Neural networks are used for decision making in both machine learning and deep learning, but the deep learning framework here is quite different, using several nonlinear layers that generate complexity to obtain more precision, whereas a machine learning system is implemented linearly. In the present paper, those technologies were explored in order to provide researchers with a clear vision in the field of deep learning for future research.
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Patil, T., Pandey, S., Visrani, K. (2021). A Review on Basic Deep Learning Technologies and Applications. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_61
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DOI: https://doi.org/10.1007/978-981-15-4474-3_61
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