深度学习与机器学习——Python数据结构与数学基础：从理论到实践 (Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice)

Silin Chen,Ziqian Bi,Junyu Liu,Benji Peng,Sen Zhang,Xuanhe Pan,Jiawei Xu,Jinlang Wang,Keyu Chen,Caitlyn Heqi Yin,Pohsun Feng,Yizhu Wen,Tianyang Wang,Ming Li,Jintao Ren,Qian Niu,Xinyuan Song,Ming Liu

from arxiv, 298 pages

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.

翻译：本书全面介绍了机器学习（ML）与深度学习（DL）的基础概念。它弥合了理论数学与实际应用之间的鸿沟，重点以Python作为实现关键算法与数据结构的主要编程语言。本书涵盖广泛主题，包括基础与高级Python编程、基本数学运算、矩阵运算、线性代数，以及对训练ML与DL模型至关重要的优化技术。同时探讨了神经网络、优化算法、频域方法等高级主题，以及大语言模型（LLMs）与人工智能（AI）在大数据管理中的实际应用。本书面向初学者与进阶学习者，强调数学原理在开发可扩展AI解决方案中的关键作用。全书提供丰富的实践案例与Python代码，确保读者获得将理论知识应用于解决ML、DL及大数据分析中复杂问题的实际经验。