International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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A Conceptual Framework for Deep Learning Algorithms and Their Applications

Author(s) Jayeshkumar Madhubhai Patel
Country India
Abstract Deep learning plays an important role in our lives. It has already had a huge impact in areas such as cancer diagnosis, personalized medicine, self-driving cars, predictive analytics and speech recognition. Intuitive hand-crafted features used in traditional learning, classification, and model recognition systems are valuable for large data sets. In many cases, depending on the complexity of the problem, DL can also overcome the limitations of sparse networks in the past that prevent effective training and spatial representation of high-dimensional training data. A deep network uses many layers (deep) of units with sophisticated algorithms and architecture. This paper reviews several optimization methods to improve training accuracy and reduce training time. We delve into the math behind the training algorithms used in the latest deep networks. We describe current failures, improvements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep networks, regular networks, reinforcement learning, differential autoencoders, etc.
Keywords Machine learning algorithm, optimization, artificial intelligence, deep neural network architectures, convolution neural network, backpropagation, supervised and unsupervised learning.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-25
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33737
Short DOI https://doi.org/g8w2wr
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E-ISSN 2582-2160
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