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Mastering Algorithms: From Theory to Practice : A Complete Guide to Problem Solving, Design Patterns, and Computational Thinking
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- FormatePub
- ISBN8232582159
- EAN9798232582159
- Date de parution18/01/2026
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurDraft2Digital
Résumé
Mastering Algorithms: From Theory to Practice is a complete, modern guide to algorithmic thinking for students, software engineers, and self-learners who want more than memorization they want understanding. Algorithms are the foundation of computer science, yet they are often taught as abstract formulas or disconnected code snippets. This book takes a different approach. It presents algorithms as practical problem-solving tools, showing how they are designed, analyzed, optimized, and applied in real systems.
Beginning with core concepts such as algorithmic thinking, efficiency analysis, and design strategies, the book gradually progresses to advanced topics including dynamic programming, greedy methods, graph algorithms, machine learning algorithms, distributed systems, streaming computation, and approximation techniques. Each topic is grounded in intuition, visual reasoning, and real-world case studies.
Unlike language-specific guides, Mastering Algorithms focuses on transferable ideas that apply across programming languages and platforms. Mathematical concepts are introduced carefully and always tied to practical relevance, making the material accessible without sacrificing rigor. The book also addresses modern concerns such as scalability, randomness, ethics, and societal impact, preparing readers to design algorithms responsibly in today's data-driven world.
Whether you are studying computer science, preparing for technical interviews, or strengthening your engineering foundation, Mastering Algorithms provides the clarity, structure, and depth needed to think algorithmically with confidence.
Beginning with core concepts such as algorithmic thinking, efficiency analysis, and design strategies, the book gradually progresses to advanced topics including dynamic programming, greedy methods, graph algorithms, machine learning algorithms, distributed systems, streaming computation, and approximation techniques. Each topic is grounded in intuition, visual reasoning, and real-world case studies.
Unlike language-specific guides, Mastering Algorithms focuses on transferable ideas that apply across programming languages and platforms. Mathematical concepts are introduced carefully and always tied to practical relevance, making the material accessible without sacrificing rigor. The book also addresses modern concerns such as scalability, randomness, ethics, and societal impact, preparing readers to design algorithms responsibly in today's data-driven world.
Whether you are studying computer science, preparing for technical interviews, or strengthening your engineering foundation, Mastering Algorithms provides the clarity, structure, and depth needed to think algorithmically with confidence.