从数学基础到代码实践，手把手教你用Python掌握机器学习核心
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
- 以Python为工具，系统讲解机器学习数学基础与数据结构
- 涵盖线性代数、优化算法等关键理论，支撑模型训练与调优
- 适合零基础入门或想夯实理论的开发者与研究人员
本书全面介绍机器学习（ML）与深度学习（DL）的基础概念，弥合理论数学与实际应用之间的鸿沟，以Python为主要编程语言实现核心算法与数据结构。内容涵盖基础与高级Python编程、基本数学运算、矩阵运算、线性代数及训练模型所必需的优化技术。深入探讨神经网络、优化算法、频域方法等高级主题，并结合大语言模型（LLMs）和人工智能（AI）在大数据管理中的真实应用场景。全书注重数学原理在构建可扩展AI解决方案中的关键作用，贯穿大量实用案例与Python代码，帮助读者将理论知识应用于解决机器学习、深度学习与大数据分析中的复杂问题。
原文摘要 · Abstract (English)
This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathematical operations, matrix operations, linear algebra, and optimization techniques crucial for training ML and DL models. Advanced subjects like neural networks, optimization algorithms, and frequency domain methods are also explored, along with real-world applications of large language models (LLMs) and artificial intelligence (AI) in big data management. Designed for both beginners and advanced learners, the book emphasizes the critical role of mathematical principles in developing scalable AI solutions. Practical examples and Python code are provided throughout, ensuring readers gain hands-on experience in applying theoretical knowledge to solve complex problems in ML, DL, and big data analytics.
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