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Learn common strategies for efficient problem solving -- such as divide and conquer, dynamic programming, and greedy approaches.
| Publisher | O'Reilly Media |
| Publication date | September 21, 2021 |
| Edition | 1st |
| Language | English |
| Print length | 278 pages |
| ISBN-10 | 1492091065 |
| ISBN-13 | 978-1492091066 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 6.75 x 0.5 x 9 inches (17.1 x 1.3 x 22.9 cm) |
Ideal for beginners wanting to understand algorithms and improve their coding skills in practical applications.
A great resource for experienced programmers seeking to enhance their algorithm knowledge and optimize their code.
Beneficial for university students needing a practical guide to algorithms for coursework and projects.
Not suitable for those with no programming background, as it assumes prior knowledge of coding concepts.
Learning Algorithms: A Programmer's Guide to Writing Better Code
In the realm of computer science literature, this particular book manages to carve out a niche that appeals to both beginners and those with some programming experience. Praised for its ability to demystify complex concepts, the book strikes a harmonious balance between traditional textbooks and more simplistic introductions like "Grokking Algorithms." It presents algorithmic principles in an engaging manner, laden with practical examples primarily using Python. While the Python examples are generally well-received, some readers express a desire for more language-agnostic explanations, illustrating the book's slight over-reliance on Pythonic idioms. Feedback highlights the book as a valuable resource for non-computer science majors who are actively programming and seek to deepen their understanding of algorithms. However, new learners should be forewarned about certain omissions in implementations, such as a complete linked list structure, which might lead to confusion for those lacking a strong foundation in computer science fundamentals. Overall, the book is lauded as an excellent supplemental material for anyone keen on improving their algorithmic proficiency, but it also advises careful navigation when applying concepts in varied programming languages. **
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