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Item #:
225782249
Elements of Deep Learning
Item #:
225782249
IQD 202860
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Elements of Deep Learning emphasizes both theoretical depth and real-world application with PyTorch-based code examples.
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What Stands Out
Comprehensive Coverage
Covers key concepts of deep learning, including neural networks, optimization, and practical applications, making it suitable for both beginners and advanced learners seeking to deepen their knowledge.
Hands-On Approach
Includes practical examples and exercises that encourage users to apply theoretical knowledge to real-world scenarios, enhancing understanding and retention of deep learning techniques.
Expert Insights
Authored by leading experts in the field, providing authoritative perspectives and cutting-edge research insights that help learners stay updated with the latest trends and developments in deep learning.
Product Details
| Publisher | Springer |
| Publication date | May 8, 2026 |
| Language | English |
| Print length | 596 pages |
| ISBN-10 | 3032107377 |
| ISBN-13 | 978-3032107374 |
| Item Weight | 3.15 pounds (1.43 kg) |
| Dimensions | 7.28 x 1.22 x 10.1 inches (18.5 x 3.1 x 25.7 cm) |
Product Description
Elements of Deep Learning
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Intelligence & Semantics Editorial Review
Elements of Deep Learning offers a comprehensive exploration of advanced concepts in deep learning, presented in a 596-page volume by Springer. This book, published on May 8, 2026, is an essential read for those wishing to deepen their understanding of machine learning techniques. With its clear structure and expert insight, it caters to both newcomers and seasoned practitioners in the field. The robust dimensions of the book (7.28 x 1.22 x 10.1 inches) enhance its usability, making it an ideal addition to any academic or professional library.
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Pros
- In-depth coverage of deep learning topics
- Ideal for both beginners and experts
- Comprehensive 596-page resource
- Includes practical applications and examples
- Published by renowned Springer publishers
Cons
- Print weight may be cumbersome for some readers
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IQD 202860
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Features & Benefits
- Comprehensive introduction to deep learning and neural networks.
- Covers essential concepts, classic architectures, and advanced models.
- Focus on practical implementation using PyTorch.
- Structured into five sections for clear learning progression.
- Ideal for students and professionals in engineering and computer science.
- Accessible explanations with minimal prerequisites in math.
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