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Sparse Estimation with Math and Python: 100 Exercises for Building Logic
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The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience.
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Detalji o proizvodu
| Publisher | Springer |
| Publication date | October 31, 2021 |
| Edition | 1st ed. 2021 |
| Language | English |
| Print length | 256 pages |
| ISBN-10 | 9811614377 |
| ISBN-13 | 978-9811614378 |
| Item Weight | 14 ounces (396.9 grams) |
| Dimensions | 6.1 x 0.58 x 9.25 inches (15.5 x 1.5 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for individuals aiming to strengthen their skill set in data analysis and statistical modeling using Python.
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Students in Mathematics
Beneficial for students studying mathematics who want practical applications to reinforce their theoretical understanding.
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Programming Enthusiasts
Great for those who enjoy coding and want to apply their skills in mathematical problem-solving with practical exercises.
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Beginners in Programming
Not suitable for absolute beginners lacking foundational programming knowledge, as it assumes prior experience in Python.
OPIS PROIZVODA
Sparse Estimation with Math and Python: 100 Exercises for Building Logic
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Intelligence & Semantics Editorial Review
Sparse Estimation with Math and Python: 100 Exercises for Building Logic is an engaging resource published by Springer, designed to enhance your logical reasoning skills through practical exercises. Covering a range of topics, this 256-page book is ideal for anyone looking to deepen their understanding of sparse estimation techniques, while being accessible in English with a well-organized layout. Readers have appreciated the variety of exercises that challenge and develop problem-solving abilities, making it an excellent supplement for those advancing their knowledge in math and programming. Whether you are a student or a professional, the clarity and structure of this book make it a valuable educational tool.
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Pros
- Comprehensive exercises for skills development
- Well-structured and easy to follow
- Suitable for students and professionals
- Enhances logical reasoning capabilities
- Engaging content from a reputable publisher
Cons
- Could benefit from additional examples or solutions
Product Price History
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Karakteristike i prednosti
- Learn the core principles of sparse estimation through mathematical logic.
- Includes 100 exercises for hands-on practice and mastery.
- Self-contained approach makes it ideal for independent study.
- Suitable for undergraduate and graduate courses with structured content.
- Provides mathematical proofs and Python programs without dependencies.
- A valuable resource for data scientists, ML engineers, and researchers.
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