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Record Nr. |
UNINA9911034866003321 |
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Autore |
Shi Chenyang |
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Titolo |
Mastering Algorithms with Python : A Practical Approach to Problem Solving and Python Implementation / / by Chenyang Shi |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (228 pages) |
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Collana |
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Professional and Applied Computing Series |
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Disciplina |
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Soggetti |
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Distributed algorithms |
Python (Computer program language) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Chapter 1: Recursion -- Chapter 2: Divide and Conquer -- Chapter 3: Greedy Algorithm -- Chapter 4: Dynamic Programming -- Chapter 5: RSA Cryptosystem -- Chapter 6: Monte Carlo -- Chapter 7: A Tale of Ten Cities -- Chapter 8: Chess -- Appendix: A Quick Review of Python -- Appendix B: Environment Setup and Package Installation -- Appendix C: References. |
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Sommario/riassunto |
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Gain a solid understanding of algorithms and improve your problem-solving abilities using Python code. With practical examples and clear explanations, this book bridges the gap between dense academic texts and overly simple industry guides. Focusing on the logic behind essential algorithms such as Breadth First Search (BFS), Depth First Search (DFS), Divide-and-Conquer, Greedy Methods, and Dynamic Programming, the book provides ample examples, from easy to more advanced. By connecting these concepts to real-world examples, such as chess strategies and the Seam Carving, the book helps readers better grasp and apply algorithms. Each chapter also includes fully implemented Python code, making it a practical reference. Mastering Algorithms with Python is ideal for IT professionals looking to enhance their skills and approach algorithms with clarity and confidence. What You Will Learn · Understand foundational algorithms such as BFS, DFS, Divide-and-Conquer, Greedy Methods, Dynamic Programming through practical examples · Implement algorithms in Python with step-by-step guidance and fully functional code for future reference · Build a solid |
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foundation in advanced concepts such as Minimum Spanning Trees, Fast Fourier Transform, and Monte Carlo Tree Search · Quickly review Python essentials, including data types, flow control, generators, decorators, and classes to enhance your algorithmic understanding. |
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