手把手教你看懂深度学习如何驱动大数据分析与管理。
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer
- 用可视化和案例讲透神经网络原理，降低学习门槛。
- 解析CNN、Transformer、BERT等核心模型应用效果。
- 适合想入行AI或提升数据处理能力的初学者与从业者。
本书探讨人工智能（AI）、机器学习（ML）和深度学习（DL）在推动大数据分析与管理中的作用。聚焦简化深度学习背后的复杂数学概念，通过直观可视化和实际案例帮助读者理解神经网络及卷积神经网络（CNN）等技术的工作机制。介绍多种经典模型与技术，如Transformer、GPT、ResNet、BERT和YOLO，突出其在自然语言处理、图像识别和自动驾驶等领域的应用。强调预训练模型对提升性能与准确率的重要性，并提供在真实场景中应用这些模型的指导。同时概述关键的大数据管理技术，包括SQL与NoSQL数据库，以及分布式计算框架如Apache Hadoop和Spark，解释它们在处理海量数据中的重要性。最终强调掌握深度学习与大数据管理技能对未来职业发展的关键价值，是初学者与专业人士的重要参考资源。
原文摘要 · Abstract (English)
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.
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