Deep Learning and Machine Learning - Python Data Structures and Mathematics Fundamental: From Theory to Practice

2024-10-22

arXiv.org

AI Summary

To address the disconnect between mathematical foundations and engineering implementation in AI education, this paper proposes a Python-based pedagogical framework integrating mathematics and programming. Methodologically, it introduces a “mathematics–code–model” tripartite paradigm that systematically unifies linear algebra, optimization theory, and deep learning algorithms; notably, it is the first to deeply integrate frequency-domain analysis techniques with practical large language model (LLM) case studies. Technical implementation covers NumPy/SciPy-based matrix computation, gradient-based optimization, hand-coded feedforward and recurrent neural networks, and LLM API development. The primary contribution lies in bridging the competency gap between novice learners and industry-ready AI practitioners, delivering executable, full-stack AI code and real-world application scenarios. Experimental results demonstrate significant improvements in learners’ practical proficiency in modeling, hyperparameter tuning, and intelligent big-data analysis.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Bridging theoretical mathematics and practical application in machine learning.
• Implementing key algorithms and data structures using Python programming.
• Applying mathematical principles to develop scalable AI solutions.

Innovation

• Bridging theoretical mathematics with practical Python applications
• Covering fundamental to advanced ML and DL algorithms
• Providing hands-on Python code for real-world AI solutions