Bject Oriented Programming In Python For Mathematicians
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Object-Oriented Programming in Python for Mathematicians
This book is for mathematicians, scientists, and engineers who have learned the very basics of programming in Python, and who would like to become more capable programmers. You will learn the higher level programming concepts such as objects, inheritance, and abstract data types needed to elegantly create more advanced programs. At the same time, emphasis is placed on programming skills such as good style, so you learn to write code that you and others find easy to understand, and interpreting and debugging errors. If you find yourself baffled by the pages of error messages that Python emits, and would like to make sense of them, then this book is for you. Learning the material is supported by explanatory videos throughout and skeleton codes for all of the exercises, including automated tests of your work. The book takes a mathematician's view of programming, introducing higher level programming abstractions by analogy with the abstract objects that make up higher mathematics. Examples and exercises are chosen from across mathematics, though the actual mathematical knowledge required to understand this book is limited to differentiating functions of one variable. Contents Introduction: abstraction in mathematics and programming Programs in files Objects and abstraction A matter of style Abstract data types Errors and exceptions Inheritance and composition Debugging and testing Trees and directed acyclic graphs Further object-oriented features
Python for Mathematical Thinking
This book offers a rigorous yet approachable pathway to applying Python for mathematical problem-solving, spanning foundational concepts to advanced theoretical frameworks. It bridges the gap between abstract mathematics and computational execution, guiding readers through a logically structured, step-by-step journey. Emphasizing mathematical reasoning, symbolic computation, and real-world problem modeling, it equips readers to analyze, simulate, and visualize complex structures with clarity and efficiency. Ideal for students, researchers, and professionals in Mathematics, Data Science, AI, Physics, and Computational Science, it cultivates both programming skill and deep mathematical intuition.
Mathematics and Programming for Machine Learning with R
Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R. The book begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to the coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with machine learning based on artificial neural networks. The first half of the book does not require mathematical sophistication, although familiarity with probability and statistics would be helpful. The second half assumes the reader is familiar with at least one semester of calculus. The text guides novice R programmers through algorithms and their application and along the way; the reader gains programming confidence in tackling advanced R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of machine learning algorithms