Deep Learning for Vision Systems (Paperback)
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Product Details
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Nonfiction |
| Publication date | November, 2020 |
| Pages | 480 |
| Reading level | Professional & Vocational |
| Subgenre | Computers |
| Edition | 1st Edition |
| Publisher | Manning |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Educational level | High School |
| Editor | Sabyasachi Bhattacharya, Ursula Heinzelmann, Visalakshi Menon, Salil Misra |
| Digital file format | PDF, Kindle, ePub |
| Digital reader format | PDF, Kindle, ePub |
| Digital audio file format | PDF, Kindle, ePub |
| Assembled product height | 1.2 in (3 cm) |
| Assembled product weight | 1.7 lb (770 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
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Machine Learning Enthusiasts
Individuals eager to explore advanced concepts and practical applications of deep learning in visual recognition systems.
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Academics and Researchers
Students and professionals seeking a comprehensive guide to deep learning techniques for image processing and computer vision.
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Industry Professionals
Technology developers and engineers working on vision-based AI projects requiring practical insights and implementation strategies.
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Beginners in AI
Total newcomers to AI or deep learning may find advanced concepts challenging without prior knowledge or experience.
Product Description
Deep Learning for Vision Systems (Paperback)
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Mohamed Elgendy All Books Editorial Review
Deep Learning for Vision Systems (Paperback) is a comprehensive non-fiction resource aimed at professionals and vocational learners in the technology sector. Released in November 2020 by Manning, this first edition spans 480 pages and delves deeply into the intricacies of computer vision through the lens of deep learning. The book, edited by experts in the field, ensures that readers engage with cutting-edge concepts and practices in a structured manner. Ideal for high school levels and above, this paperback is also available in various digital formats such as PDF, Kindle, and ePub, accommodating diverse reading preferences.
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Pros
- Comprehensive coverage of deep learning techniques
- Ideal for professionals in technology
- Available in multiple digital formats
- Suitable for high school educational levels
- Edited by industry experts
Cons
- No visual illustrations to accompany text
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