Algorithm

Mastering Algorithms: From Fundamentals to Advanced Applications for Students and Professionals provides a structured journey through algorithm fundamentals, complexity analysis, design strategies, optimization techniques, and advanced graph algorithms.Explore Divide and Conquer, Greedy Algorithms, Dynamic Programming, Backtracking, Branch and Bound, graph traversal, shortest paths.

Mastering Data Structures Using C – QUESTION BANK: 1000+ Conceptual Questions for Students and Professionals is a comprehensive practice resource featuring MCQs, short-answer questions, detailed mid-length questions with answers, and solved programming questions.Covering arrays, linked lists, stacks, queues, trees, graphs, searching, sorting, hashing, file organization, recursion, backtracking.

Data Structures in C: A Comprehensive Guide for Students transforms complex data-structure concepts into clear explanations, step-by-step algorithms, practical C programs, detailed code walkthroughs, real-world applications, and hands-on exercises.From arrays, linked lists, stacks, and queues to trees, graphs, searching, sorting, recursion

Programming Ideas is an extensive collection of fun and fascinating programming projects for intermediate to advanced programmers. It closes the gap between introductory programming books and computer science textbooks by showing you how to solve a wide range of practical problems, from drawing fractals to compressing files and computing Pi, and carefully explains both the required algorithms and techniques as well as the resulting programs.

Learn how to create profitable Facebook Ads and Google Ads that generate more traffic, leads, and sales. This practical step-by-step guide teaches you how to target the right audience, optimize your campaigns, reduce wasted ad spend, and maximize your return on investment.

Master Discrete Mathematics — the foundation of Computer Science. Comprehensive guide specially designed for BCA & MCA students with detailed explanations, solved examples, real-world applications, and exam-focused practice. Perfect for university exams, GATE, and interviews.

How does code actually run under the hood? Stop guessing and master the hidden physics of execution. Learn how Python and Java truly manage memory, layout data structures, and drive high-performance algorithms.

Artificial Intelligence doesn't become intelligent by chance—it learns through optimization.Every neural network improves because of Gradient Descent.Every recommendation system depends on matrix factorization.Every deep learning model relies on numerical computation.Numerical Methods with Artificial Intelligence Applications – Volume 2 takes you beyond the fundamentals and into the advanced mathematical engines driving modern AI.Discover how optimization algorithms like Adam, RMSProp, Newton's Method, and Conjugate Gradient train intelligent systems. Explore numerical linear algebra, probabilistic AI, evolutionary computation, Python implementations, and real-world AI case studies that reveal the mathematics behind today's most powerful technologies.Whether you're building machine learning models, conducting AI research, or strengthening your computational skills, this volume equips you with the advanced numerical techniques essential for the next generation of intelligent systems.Master the algorithms that teach machines how to learn.

Artificial Intelligence is built on mathematics—and numerical methods are at its core.What happens behind gradient descent? How do optimization algorithms converge? Why are interpolation, differentiation, and integration essential for machine learning?Discover the mathematical engine that powers modern AI.Numerical Methods with Artificial Intelligence Applications (Volume 1) transforms traditional numerical methods into practical AI tools through real-world examples, optimization techniques, and machine learning applications.Whether you're a student, researcher, educator, or AI enthusiast, this book will help you understand the algorithms that make intelligent systems possible.

I wrote this book to show exactly what happens when you add Rust to the kernel. Not as a toy. Not as a single driver in a conference demo. It's a full module that covers process management, memory allocation, file systems, networking, device drivers, inter-process communication, and even machine learning inference. It's all built on safe abstractions, all compiled into one loadable binary, all tested on a running kernel. You'll find that each chapter adds new source files to the same module.

Why learn one programming language when you can understand three at once? This book introduces the foundations of programming through a powerful side-by-side comparison of Python, Java, and C#, enabling readers to build strong conceptual understanding, transferable coding skills, and a solid pathway toward modern software engineering.

 Why Should You Study This Book?Comprehensive Coverage: This book covers all essential C programming concepts, from the basics to intermediate topics, making it suitable for students across various academic levels, including Class 12, graduates, and MCA students.Clear Explanations: The concepts are presented in a simple, easy-to-understand manner. Whether you are a novice or have some programming experience, you’ll find the explanations clear and easy to follow.Practical Focus: It includes numerous hands-on examples and practice problems to reinforce the concepts. With each chapter, you will have the opportunity to write real C programs, which will help you solidify your understanding and develop practical programming skills.Real-World Applications: The book does not just cover theory. It guides you through creating practical programs such as a basic calculator, a library management system, and even a text editor. These projects provide a real-world context to what you’re learning, making it easier to grasp how C programming is used in various applications.Skill Development: Learning C programming will sharpen your problem-solving abilities, logical thinking, and analytical skills. These skills are essential not only for programming but also for tackling complex challenges in various fields of study and work.Error Handling and Debugging: The book emphasizes the importance of writing error-free and optimized code. It introduces common pitfalls and debugging techniques, helping students become efficient and error-resistant programmers.Preparation for Further Studies: If you're planning to learn advanced topics like data structures, algorithms, or even system programming, this book provides the foundation you'll need. Mastering C will give you a strong base for learning other programming languages and advanced computing concepts.

By diving into this book, students will:1.     Master Supervised Learning Fundamentals Gain strong conceptual understanding and practical competence.2.     Evate & Deploy Models Effectively Understand how to build, validate, interpret, and deploy high-quality models.3.     Think Critically about AI Systems Develop ethical awareness and critical reasoning regarding data bias and model behavior.

·        Comprehensive Learning Path: The book starts with basics and gradually leads you to advanced topics, making it accessible for beginners and challenging for advanced learners.·        Contextual AI Applications: Every concept is illustrated with AI and ML examples, ensuring relevance and immediate applicability.·        Enhanced Understanding of AI Models: Knowing data structures like trees and graphs clarifies how decision trees or knowledge graphs operate internally, boosting your model-building skills.·        Algorithm Efficiency Awareness: Understanding algorithm complexity and heuristics allows you to write optimized AI programs that can handle large datasets and real-time processing.·        Practical Coding Exercises: With implementations in Python, you will develop a coding mindset essential for AI practitioners.·        Preparation for Research and Development: The book equips you to contribute to AI research and innovate new algorithms or improve existing ones.

An introduction to actor-critic algorithms as dynamical systems: featuring hand-computable examples, fast-slow reductions, and machine-checked Lean 4 proofs