Mathematics and Deep Learning: Powering Big Data Analytic and Artificial Intelligence

Dear Colleagues,

"Mathematics and Deep Learning: Powering Big Data Analytic and Artificial Intelligence" provides a focus on the mathematical underpinnings of deep learning in the context of big data analytics and AI. The key topics of interest include, but are not limited to, those briefly summarized below:

• Mathematical Models in Deep Learning: Understanding the mathematical basis of deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
• Deep Learning Frameworks: Exploring the mathematical aspects of popular deep learning frameworks, such as TensorFlow and PyTorch.
• Big Data Analytics with Mathematical Tools: Discussing how mathematics enables the analysis and interpretation of large-scale datasets in the context of deep learning and AI.
• Ethical and Fair AI: Addressing the mathematical considerations related to fairness, bias, and ethics in deep learning and AI.
• Convolutional Neural Networks (CNNs): Discussing the mathematical principles behind CNNs, which are widely used in image and video analysis.
• Recurrent Neural Networks (RNNs): Exploring the mathematical structures of RNNs and their applications in sequence data analysis.
• Applications of Deep Learning in Big Data Analytics: Real-world examples of how deep learning and mathematics are used for data analysis, pattern recognition, computer vision, and recommendation systems.
• Applications in AI: Real-world use cases showcasing how deep learning, driven by mathematics, powers AI in areas such as computer vision, natural language processing, and autonomous systems.
• Real-World Applications: Case studies and practical applications where mathematical principles and machine learning are used to solve real-world problems in healthcare, finance, marketing, and other domains.
• Future Trends: Examining emerging mathematical techniques and technologies that drive advancements in deep learning and big data analytics.

This Special Issue will present a deep dive into the mathematical aspects of deep learning and its critical role in the broader field of big data analytics and AI. We welcome contributions of research articles covering the above topics, each of which can provide a comprehensive understanding of how mathematics powers deep learning and its applications in data analytics and AI.

Prof. Dr. Dong-Her Shih
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• mathematical foundations
• big data
• data mining
• machine learning
• deep learning
• predictive analytics
• prescriptive analytics
• data science
• AI ethics
• AI applications
• emerging technologies