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.

翻译：本书探讨了人工智能（AI）、机器学习（ML）和深度学习（DL）在推动大数据分析与管理进步中的作用。本书着重简化深度学习背后的复杂数学概念，提供直观的可视化展示和实际案例研究，帮助读者理解神经网络及卷积神经网络（CNN）等技术的工作原理。书中介绍了几种经典模型与技术，如Transformer、GPT、ResNet、BERT和YOLO，重点阐述了它们在自然语言处理、图像识别和自动驾驶等领域的应用。本书还强调了预训练模型的重要性及其如何提升模型性能与精度，并指导如何在各种实际场景中应用这些模型。此外，本书概述了关键的大数据管理技术，如SQL和NoSQL数据库，以及Apache Hadoop和Spark等分布式计算框架，阐释了它们在管理和处理海量数据方面的重要性。最后，本书强调掌握深度学习与大数据管理技能作为未来劳动力关键工具的价值，使其成为初学者和经验丰富的专业人士的必备资源。