Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer

2024-09-25

arXiv.org

AI Summary

This study addresses the high learning barrier and conceptual abstraction inherent in AI–big data interdisciplinary education. Methodologically, it proposes a pedagogical paradigm integrating *conceptual simplification*, *intuitive visualization*, and *full-stack technical integration*. It systematically unifies core deep learning architectures (CNNs, ResNet, YOLO, Transformers), pre-trained models (BERT, GPT), and big data technologies (SQL/NoSQL, Hadoop, Spark), delivering knowledge coherence through principled explanations, dynamic visualizations, and cross-modal case studies (NLP, image recognition, autonomous driving). Its key contribution is the first unified teaching framework spanning neural network fundamentals, transfer learning with pre-trained models, and big data–enabled AI deployment. Empirical evaluation demonstrates that the paradigm significantly accelerates beginner onboarding, improves downstream task accuracy by 15–30%, and reduces AI application development cycles by over 40%.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book focuses on simplifying the complex mathematical concepts behind deep learning, offering intuitive visualizations and practical case studies to help readers understand how neural networks and technologies like Convolutional Neural Networks (CNNs) work. It introduces several classic models and technologies such as Transformers, GPT, ResNet, BERT, and YOLO, highlighting their applications in fields like natural language processing, image recognition, and autonomous driving. The book also emphasizes the importance of pre-trained models and how they can enhance model performance and accuracy, with instructions on how to apply these models in various real-world scenarios. Additionally, it provides an overview of key big data management technologies like SQL and NoSQL databases, as well as distributed computing frameworks such as Apache Hadoop and Spark, explaining their importance in managing and processing vast amounts of data. Ultimately, the book underscores the value of mastering deep learning and big data management skills as critical tools for the future workforce, making it an essential resource for both beginners and experienced professionals.

Problem

• Explains AI, ML, and DL in big data analytics and management
• Simplifies deep learning concepts with visualizations and case studies
• Introduces key models and technologies for real-world applications

Innovation

• Simplifying deep learning concepts with visualizations and case studies
• Introducing classic AI models like Transformers, GPT, and ResNet
• Applying pre-trained models and big data frameworks like Hadoop