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150587812
Synthesis Lectures on Computer Architect Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, (Paperback)
84% of respondents would recommend this to a friend
товар №:
150587812
AZN 114
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This book provides a solid understanding of the design, training, and applications of deep learning algorithms for industry, emphasizing collaboration across various engineering disciplines.
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What Stands Out
Comprehensive Coverage
This book offers an in-depth exploration of algorithms, compilers, and processors tailored for large-scale deep learning systems, making it an essential resource for computer architecture enthusiasts and professionals.
Expert Contributions
Written by leading experts in the field, it provides insights into current trends and breakthroughs, ensuring readers gain access to up-to-date knowledge that's critical for advancing their understanding of deep learning systems.
Practical Applications
Focusing on real-world applications, the text bridges the gap between theoretical concepts and practical implementations, empowering readers to apply learned techniques to optimize deep learning performance in production environments.
Информация о продукте
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | October, 2020 |
| Pages | 245 |
| Subgenre | Computer Architecture |
| Series title | Synthesis Lectures on Computer Architecture |
| Edition | 1 |
| Publisher | Springer International Publishing |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.50 x 0.56 x 9.25 in (19.1 x 1.4 x 23.5 cm) |
| Assembled product weight | 1.03 lb (470 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Computer Scientists
Ideal for those researching deep learning algorithms and their integration into computer architecture for real-world applications.
-
Software Engineers
Beneficial for software engineers creating deep learning systems, requiring knowledge of compilers and hardware interactions.
-
Graduate Students
Excellent resource for graduate students specializing in computer architecture or machine learning, offering comprehensive insights.
-
Casual Readers
Not suitable for casual readers seeking general knowledge; the material is technical and requires prior understanding.
ОПИСАНИЕ ТОВАРА
Synthesis Lectures on Computer Architect Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, (Paperback)
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Andres Rodriguez All Books Editorial Review
Synthesis Lectures on Computer Architect Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production provides an insightful exploration into the realm of computing. This non-fiction paperback, published by Springer International Publishing in October 2020, spans 245 pages and is a part of the Synthesis Lectures on Computer Architecture series. It delves into specific topics such as algorithms and compilers, making it highly relevant for those interested in technology and computer architecture. Readers appreciate its thorough examination of techniques for optimizing large-scale production systems, making it a recommended read for professionals and students alike.
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Плюсы
- Comprehensive coverage of deep learning systems
- Well-structured and informative content
- Ideal for both students and professionals
- Covers algorithms and compiler optimization
- Published by a reputable academic publisher
Минусы
- May not be suitable for casual readers
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AZN 114
Закажите сейчас и получите товар приблизительно Суббота, Октябрь 31
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Особенности и преимущества
- Learn the fundamentals of deep learning systems and algorithms.
- Understand compiler techniques to optimize deep learning code for hardware.
- Explore essential hardware features for today's and future deep learning models.
- Facilitate collaboration among data scientists, hardware designers, and performance engineers.
- Incorporate practical techniques to design and accelerate model performance.
- Access industry insights and examples from leading high-technology companies.
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