Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866908695788519424 |
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| author | Peng, Benji Pan, Xuanhe Wen, Yizhu Bi, Ziqian Chen, Keyu Li, Ming Liu, Ming Niu, Qian Liu, Junyu Wang, Jinlang Zhang, Sen Xu, Jiawei Song, Xinyuan Jiang, Zekun Wang, Tianyang Feng, Pohsun |
| author_facet | Peng, Benji Pan, Xuanhe Wen, Yizhu Bi, Ziqian Chen, Keyu Li, Ming Liu, Ming Niu, Qian Liu, Junyu Wang, Jinlang Zhang, Sen Xu, Jiawei Song, Xinyuan Jiang, Zekun Wang, Tianyang Feng, Pohsun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17120 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer Peng, Benji Pan, Xuanhe Wen, Yizhu Bi, Ziqian Chen, Keyu Li, Ming Liu, Ming Niu, Qian Liu, Junyu Wang, Jinlang Zhang, Sen Xu, Jiawei Song, Xinyuan Jiang, Zekun Wang, Tianyang Feng, Pohsun Computation and Language Machine Learning 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. |
| title | Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2409.17120 |