Advancements in Deep Learning: A Comprehensive Review

Deep learning is a recent area of study in machine learning (ML). There are numerous hidden layers of artificial neural networks. High level model abstractions and nonlinear transformations are used in massive databases as part of the deep learning technique. Recent significant advancements in deep learning architectures in a range of fields have had a significant impact on artificial intelligence. This article provides a modern overview of the contributions and cutting-edge applications of deep learning. Deep learning techniques have been applied in major applications, as explained in the review that follows. Also reviewed and contrasted with those of more conventional algorithms in common applications are the advantages and drawbacks of the deep learning technique, as well as its hierarchy of layers and nonlinear operations. A detailed overview of the original concept and the expanding advantages and popularity of deep learning are also included in the state-of-the-art review.

Convolutional neural network principles are applied to a hybrid NN-HMM model for speech recognition by Abdel, O. 7, 4277-4280 (2012) in Acoustics, Speech and Signal Processing.

Robot learning through the integration of machine learning and optimisation by Mosavi and Varkonyi-Koczy. 519, 349-355, Advances in Intelligent Systems and Computing (2017).

Biometrics and AI: How Face Sentinel evolves 13 times more quickly thanks to deep learning, Bannister, A. (2016).

Learning deep architectures for AI by Yoshua Bengio. 1–127 (2009), Foundations and Trends in Machine Learning.

Combining machine learning and optimisation for automated decision-making by Mosavi, Varkonyi-Koczy, and Fullsack. MCDM (2015).

DLeng and DYu Deep learning: procedures and uses. 197-387 (2014) Foundations and Trends® in Signal Processing 7

Rajesh Shinde, C. Kalpana, “Advancements in Deep Learning: A Comprehensive Review”, REST Journal on Data Analytics and Artificial Intelligence, 2(2), June 2023:65-69.