Numerical Methods with Artificial Intelligence Applications VOL-1
Foundations Algorithms and Machine Learning Optimization
Minimum price
$9.99
$19.99
You pay
Author earns
About
About the Book
Artificial Intelligence has transformed the modern technological landscape, yet behind every intelligent system lies a strong mathematical and numerical foundation. Numerical Methods with Artificial Intelligence Applications – Volume 1 bridges the gap between classical numerical computation and cutting-edge AI technologies by presenting numerical algorithms through the lens of modern machine learning, optimization, and intelligent systems.
Unlike traditional numerical methods textbooks that focus primarily on engineering mathematics, this book demonstrates how numerical computation powers today's AI applications—including optimization, regression, neural networks, probabilistic modeling, reinforcement learning, scientific computing, and data-driven decision-making.
Beginning with the fundamentals of numerical computation, floating-point arithmetic, and error analysis, the book systematically develops algorithms for solving nonlinear equations, interpolation, differentiation, numerical integration, and optimization. Each concept is connected to practical Artificial Intelligence applications, enabling readers to understand not only the mathematical theory but also its real-world relevance in modern AI systems.
The text emphasizes algorithmic thinking, convergence analysis, computational efficiency, numerical stability, and machine learning optimization techniques. Numerous examples, illustrations, algorithmic explanations, and AI-oriented case studies make the subject approachable for both beginners and advanced learners.
Whether you are a university student, researcher, AI practitioner, software engineer, or educator, this book provides a solid mathematical foundation required for developing intelligent computational systems.
What You Will Learn
• Mathematical foundations of numerical computation
• Floating-point arithmetic and computational error analysis
• Numerical stability and convergence techniques
• Root-finding algorithms and nonlinear equation solving
• Newton-Raphson, Secant, Bisection, Regula-Falsi, and Fixed Point methods
• AI optimization using numerical techniques
• Polynomial and spline interpolation
• Numerical differentiation for gradient estimation
• Numerical integration methods including Monte Carlo Integration
• Bayesian computation and probabilistic estimation
• Machine Learning optimization concepts
• Numerical algorithms used in Artificial Intelligence
• Practical computational approaches for intelligent systems
Who Should Read This Book?
This book is ideal for:
• Undergraduate and postgraduate Computer Science students
• B.Tech, BCA, MCA, MSc, M.Tech, and Data Science students
• Artificial Intelligence and Machine Learning learners
• Researchers in Computational Science
• Engineering students
• University faculty members
• Competitive examination aspirants
• Software developers
• AI engineers
• Data scientists
• Professionals interested in mathematical foundations of AI
Key Features
✔ AI-integrated Numerical Methods
✔ Beginner-friendly mathematical explanations
✔ Machine Learning-oriented examples
✔ Modern optimization techniques
✔ Step-by-step algorithmic development
✔ Practical computational approaches
✔ Industry-relevant AI applications
✔ Research-oriented content
✔ Suitable for self-learning and university courses
✔ Covers both theory and practical implementation
Why This Book is Different
Most numerical methods books stop at solving mathematical equations. This book goes one step further by showing how those same numerical techniques are used inside Artificial Intelligence, Machine Learning, Deep Learning, Data Science, and Computational Intelligence.
Readers will discover how classical mathematical algorithms continue to power modern AI systems, making this book a unique blend of mathematics, computer science, and artificial intelligence.
Whether your goal is academic excellence, AI research, software development, or building intelligent systems, this book serves as an essential foundation for understanding the numerical backbone of Artificial Intelligence.
Author
About the Author
Anshuman Kumar Mishra, M.Tech (Computer Science) Assistant Professor, Doranda College, Ranchi University
Prolific Author of 50+ Books on AI, Machine Learning & Computer Science | 20+ Years Experience
Anshuman Kumar Mishra is a dedicated educator, researcher, and highly prolific author with over 20 years of experience in Computer Science and Information Technology. Holding an M.Tech in Computer Science from BIT Mesra, he brings a rare combination of academic depth and practical teaching expertise.
Currently serving as Assistant Professor at Doranda College under Ranchi University, he has mentored thousands of students, helping them build strong foundations in programming, data science, and artificial intelligence. His student-centric teaching style emphasizes conceptual clarity, hands-on practice, and real-world application.
Anshuman is a prolific author with more than 50 books published across a wide spectrum of computer science and emerging technology domains. From foundational programming languages to advanced topics in Artificial Intelligence, Machine Learning, Reinforcement Learning, Decision Theory, and Computer Vision — his books are widely appreciated by students, educators, and professionals for their clear explanations, strong theoretical foundation, and practical approach.
His extensive body of work reflects his deep commitment to making complex subjects accessible and meaningful for learners at all levels. He is particularly recognized for creating well-structured learning paths that help readers progress from beginner to advanced levels with confidence.
Driven by the mission to democratize quality technical education, Anshuman continues to write and update books that bridge the gap between academic theory and industry practice.
When not teaching or writing, he actively follows and explores new developments in AI, Quantum Machine Learning, and Ethical Intelligence systems.
Contents
Table of Contents
The Leanpub 60 Day 100% Happiness Guarantee
Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
See full terms...
Earn $8 on a $10 Purchase, and $16 on a $20 Purchase
We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.
(Yes, some authors have already earned much more than that on Leanpub.)
In fact, authors have earned over $15 million writing, publishing and selling on Leanpub.
Learn more about writing on Leanpub
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
Learn more about Leanpub's ebook formats and where to read them
Write and Publish on Leanpub
You can use Leanpub to easily write, publish and sell in-progress and completed ebooks and online courses!
Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks.
Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. (Or, if you are producing your ebook your own way, you can even upload your own PDF and/or EPUB files and then publish with one click!) It really is that easy.