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49266543
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks Paperback
Item #:
49266543
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What Stands Out
Foundational Skills
Provides essential mathematical concepts critical for understanding and optimizing deep learning models, allowing readers to build robust knowledge for future advancements in AI.
Practical Approach
Integrates hands-on exercises that reinforce learning through practical applications, enabling readers to implement complex mathematics directly into real-world deep learning scenarios.
Accessible Format
Written in an easy-to-understand style, making advanced mathematical topics approachable for beginners and professionals, fostering a deeper understanding of neural networks.
Product Details
| Language | English |
| Publisher | Packt Publishing |
| Book Format | Paperback |
| Original Languages | English |
| Number of Pages | 364 |
| Author | Jay Dawani |
| Title | Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks |
| ISBN-13 | 9781838647292 |
| Publication Date | June, 2020 |
| ISBN-10 | 1838647295 |
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Education |
| Pub date | 2020-06-12 |
| Pages | 364 |
| Subgenre | Computers |
| Edition | Standard Edition |
| Original languages | English |
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for beginners looking to understand the mathematical concepts essential for building deep learning models.
-
Students in STEM
Perfect for university students in scientific fields who require mathematical skills in machine learning and neural networks.
-
Professionals Upskilling
Great for professionals seeking to enhance their skills in machine learning and deepen their mathematical understanding.
-
Complete Beginners
Not suitable for users with no prior knowledge of mathematics or deep learning concepts.
Product Description
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks Paperback
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Jay Dawani All Books Editorial Review
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural network is a comprehensive resource designed to help readers establish a strong mathematical background essential for understanding deep learning concepts. This non-fiction paperback, published by Packt Publishing in June 2020, spans 364 pages focusing on mathematical and statistical software relevant to computing and internet applications. Readers will appreciate that the book is structured to cater to those at a general educational level, allowing for a gradual build-up of knowledge crucial for anyone interested in deep neural networks. The engaging format and language make complex mathematical ideas more accessible, enhancing learning experiences.
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Pros
- Comprehensive coverage of deep learning mathematics
- Accessible language for learning
- Structured for general educational level
- Includes practical examples and applications
- Great resource for beginners in deep learning
Cons
- No collectible editions available
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