- Home
- Computers
- Computer Graphics
- Deep Learning for Vision Systems
Deep Learning for Vision Systems
List Price:
$49.99
- Availability: Confirm prior to ordering
- Branding: minimum 50 pieces (add’l costs below)
- Check Freight Rates (branded products only)
Branding Options (v), Availability & Lead Times
- 1-Color Imprint: $2.00 ea.
- Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
- Belly-Band Wrap: $2.50 ea. (full-color printed)
- Set-Up Charge: $45 per decoration
- Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
- Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
- Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
- RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
Product Details
Author:
Mohamed Elgendy
Format:
Paperback
Pages:
480
Publisher:
Manning (November 10, 2020)
Language:
English
ISBN-13:
9781617296192
ISBN-10:
1617296198
Weight:
28oz
Dimensions:
7.375" x 9.25" x 1.2"
File:
Eloquence-SimonSchuster_08192026_P10502905_onix30-20260819.xml
Folder:
Eloquence
List Price:
$49.99
Case Pack:
14
As low as:
$44.99
Publisher Identifier:
P-SS
Discount Code:
G
Pub Discount:
37
Imprint:
Manning
Overview
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.
Summary
Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway.
About the book
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.
What's inside
Image classification and object detection
Advanced deep learning architectures
Transfer learning and generative adversarial networks
DeepDream and neural style transfer
Visual embeddings and image search
About the reader
For intermediate Python programmers.
About the author
Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio.
Table of Contents
PART 1 - DEEP LEARNING FOUNDATION
1 Welcome to computer vision
2 Deep learning and neural networks
3 Convolutional neural networks
4 Structuring DL projects and hyperparameter tuning
PART 2 - IMAGE CLASSIFICATION AND DETECTION
5 Advanced CNN architectures
6 Transfer learning
7 Object detection with R-CNN, SSD, and YOLO
PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS
8 Generative adversarial networks (GANs)
9 DeepDream and neural style transfer
10 Visual embeddings
Summary
Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway.
About the book
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.
What's inside
Image classification and object detection
Advanced deep learning architectures
Transfer learning and generative adversarial networks
DeepDream and neural style transfer
Visual embeddings and image search
About the reader
For intermediate Python programmers.
About the author
Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio.
Table of Contents
PART 1 - DEEP LEARNING FOUNDATION
1 Welcome to computer vision
2 Deep learning and neural networks
3 Convolutional neural networks
4 Structuring DL projects and hyperparameter tuning
PART 2 - IMAGE CLASSIFICATION AND DETECTION
5 Advanced CNN architectures
6 Transfer learning
7 Object detection with R-CNN, SSD, and YOLO
PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS
8 Generative adversarial networks (GANs)
9 DeepDream and neural style transfer
10 Visual embeddings
