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Total Size:
21.7 MB
Info Hash:
126CC3F91E14F819798AB6043C6969D438727FAC
Added By:
Added:
Oct. 16, 2025, 10:29 a.m.
Stats:
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(Last updated: Oct. 16, 2025, 10:33 a.m.)
| File | Size |
|---|---|
| Code.zip | 332.5 KB |
| Shi C. Mastering Algorithms with Python. A Practical Approach...2025.pdf | 21.4 MB |
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21.7 MB
[69
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14]
2025-10-16
| Uploaded by andryold1 | Size 21.7 MB | Health [ 69 /14 ] | Added 2025-10-16 |
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11.6 MB
[25
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6]
2023-07-01
| Uploaded by indexFroggy | Size 11.6 MB | Health [ 25 /6 ] | Added 2023-07-01 |
NOTE
SOURCE: Shi C. Mastering Algorithms with Python. A Practical Approach...2025
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MEDIAINFO
Textbook in PDF format Simplifies complex algorithms by connecting them to relatable scenarios, making learning intuitive and actionable. Gain insights from a conversational narrative that bridges theory and practical application effectively. Build confidence in mastering algorithms with a resource designed for IT professionals and coding enthusiasts alike. 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 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 Who This Book Is For Software developers, data scientists, machine learning professionals and any curious learners about computer algorithms
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