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Item #:
33195672
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
86% of respondents would recommend this to a friend
Item #:
33195672
MDL 1076
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What Stands Out
Comprehensive Curriculum
Offers a thorough exploration of essential mathematical concepts, ensuring readers grasp foundational theories crucial for developing and optimizing deep learning models.
Practical Applications
Integrates hands-on exercises, allowing learners to apply mathematical principles directly to deep learning tasks, enhancing retention and understanding of complex topics.
Targeted Audience
Designed for aspiring data scientists and machine learning practitioners, making complex mathematics accessible and relevant to their specific needs in deep neural network training.
Product Details
| Publisher | Packt Publishing |
| Publication date | June 12, 2020 |
| Language | English |
| Print length | 364 pages |
| ISBN-10 | 1838647295 |
| ISBN-13 | 978-1838647292 |
| Item Weight | 1.5 pounds (680 grams) |
| Dimensions | 7.5 x 0.82 x 9.25 inches (19.1 x 2.1 x 23.5 cm) |
Who Should Buy?
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Beginners in AI
Individuals new to artificial intelligence who need foundational math knowledge for understanding deep learning concepts.
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Data Science Students
Students pursuing data science who require in-depth mathematical understanding to enhance their machine learning skills.
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Practicing Engineers
Software engineers looking to bridge the gap between their coding skills and deep learning mathematical principles.
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Advanced Practitioners
Experienced deep learning professionals may find the content too basic and lacking in advanced techniques.
Product Description
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
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Intelligence & Semantics Editorial Review
Hands-On Mathematics for Deep Learning is a good introductory text for those interested in learning about the math behind deep neural networks. The book simplifies complex mathematical concepts by breaking them down into intuitive explanations, making it accessible to readers who may not have a deep understanding of mathematics. However, some readers have criticized the book for being poorly written and containing typos, with imprecise explanations and incorrect information.
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Pros
- Good introductory text for those interested in learning about math behind deep neural networks
- Simplifies complex mathematical concepts into intuitive explanations
- Accessible to readers without a deep understanding of mathematics
Cons
- Poorly written and contains typos
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