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arXiv · 2410.19849

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
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Abstract

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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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. 2024-10-22. Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice. https://arxiv.org/abs/2410.19849

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