Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications
Pohsun Feng, Ziqian Bi, Yizhu Wen, Xuanhe Pan, Benji Peng, Ming Liu, Jiawei Xu, Keyu Chen, Junyu Liu, Caitlyn Heqi Yin, Sen Zhang, Jinlang Wang, Qian Niu, Ming Li, Tianyang Wang, Xinyuan Song, Zekun Jiang
TL;DR
This paper provides a comprehensive, practice-oriented tour of AI, ML, and DL with an emphasis on big data analytics. It connects foundational theory to hands-on tooling, including large language models (LLMs) and multimodal platforms such as ChatGPT, Claude, and Gemini, and discusses AutoML, edge computing, and fairness. It then guides readers through setting up hardware and software environments, Python-based development, and core ML methodologies, culminating in a detailed survey of neural networks, architectures, and optimization techniques. The work aims to bridge theory and practice for researchers and practitioners, highlighting actionable strategies for implementing AI-enabled data management, analytics, and decision-support across domains like healthcare, finance, and autonomous systems. Overall, it serves as a practical resource for building, deploying, and evaluating AI-powered big data solutions with an eye toward transparency and scalability.
Abstract
Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management, enabling groundbreaking advancements across diverse industries. This article delves into the foundational concepts and cutting-edge developments in these fields, with a particular focus on large language models (LLMs) and their role in natural language processing, multimodal reasoning, and autonomous decision-making. Highlighting tools such as ChatGPT, Claude, and Gemini, the discussion explores their applications in data analysis, model design, and optimization. The integration of advanced algorithms like neural networks, reinforcement learning, and generative models has enhanced the capabilities of AI systems to process, visualize, and interpret complex datasets. Additionally, the emergence of technologies like edge computing and automated machine learning (AutoML) democratizes access to AI, empowering users across skill levels to engage with intelligent systems. This work also underscores the importance of ethical considerations, transparency, and fairness in the deployment of AI technologies, paving the way for responsible innovation. Through practical insights into hardware configurations, software environments, and real-world applications, this article serves as a comprehensive resource for researchers and practitioners. By bridging theoretical underpinnings with actionable strategies, it showcases the potential of AI and LLMs to revolutionize big data management and drive meaningful advancements across domains such as healthcare, finance, and autonomous systems.