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

TL;DR

This book synthesizes Python-centric programming with foundational mathematics to teach ML/DL concepts from theory to practice. It covers Python basics, data structures, and essential linear algebra, then extends to advanced mathematical operations using NumPy, SciPy, and SymPy, including FFT, Laplace and Z transforms, and automatic differentiation. The text couples theoretical exposition with practical implementations in PyTorch and TensorFlow, and culminates in optimization techniques, batch normalization, gradient clipping, and second-order methods, all framed around real-world deep learning tasks. The step-by-step projects and exercises aim to equip readers with hands-on skills for building scalable AI systems and performing frequency-domain analyses in learning contexts.

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.