Item #:
164768501
50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning software design
90% of respondents would recommend this to a friend
Item #:
164768501
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Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition
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What Stands Out
Comprehensive Coverage
Covers essential algorithms from classic to modern, providing programmers with a well-rounded understanding of techniques across various domains like machine learning and cryptography.
Practical Applications
Focuses on real-world applications, helping programmers effectively tackle challenges in software design and data systems with proven algorithm solutions.
Up-to-Date Trends
Incorporates the latest advancements in algorithm development, ensuring readers are equipped with current knowledge relevant to today's technology landscape.
Product Details
- Explore advanced deep learning architectures and modern deep learning techniques
- Learn different programming algorithms and choose the right data structures
- Examine linear programming, page ranking, and graph algorithms, as well as machine learning algorithms
- Understand the math and logic behind deep learning models, including Large Language Models (LLMs) and sequential models
- Develop skills in applying algorithms for computational problems and implementing parallel processing techniques
- Designed for programmers seeking to understand and use algorithms for problem-solving and efficient code writing
| Publisher | Packt Publishing |
| Publication date | 29 Sept. 2023 |
| Edition | 2. |
| Language | English |
| Print length | 538 pages |
| ISBN-10 | 1803247762 |
| ISBN-13 | 978-1803247762 |
| Dimensions | 19.05 x 3.1 x 23.5 cm |
Who Should Buy?
-
Aspiring Programmers
Beginner coders will find foundational algorithms indispensable for understanding core programming concepts and practices.
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Data Scientists
Professionals in data science can benefit from insights into algorithms specific to machine learning and data systems.
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Cryptography Enthusiasts
This book provides valuable information on modern algorithms vital for anyone interested in cryptography and secure communications.
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Complete Beginners
Those with no programming experience may struggle to comprehend the complex algorithms without prior knowledge.
Product Description
50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning software design data systems and cryptography
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English edition Imran Ahmad Format: Paperback Editorial Review
50 Algorithms Every Programmer Should Know: Tackle Computer Science Challenges With Classic To Modern Algorithms In Machine Learning, Software Design, Data Systems And Cryptography is a profound resource for programmers of all levels, authored by Dr. Imran Ahmad. This book unfolds a structured approach starting from fundamental algorithms to advanced topics like machine learning and cryptography. Readers appreciate the inclusion of Python code snippets that enhance practical understanding, though some have pointed out inconsistencies and the need for clearer explanations in certain sections. Despite some criticisms, the book's emphasis on practical applications and ethical considerations showcases its relevance in today’s programming landscape, making it a recommended read for anyone aiming to deepen their algorithm knowledge.
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Pros
- Comprehensive coverage of essential algorithms
- Clear writing style for diverse expertise levels
- Includes practical Python code snippets
- Addresses ethical considerations in algorithms
- Well-structured sections from basics to advanced
Cons
- Some concepts may lack depth and clarity
Product Price History
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Features & Benefits
- Explore modern deep learning techniques
- Learn about generative AI and large language models
- Includes new chapters on LSTMs, GRUs, RNNs
- 50% new edition with updated algorithms
- Addresses hidden data biases and demystifies algorithm explainability
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