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Deep Learning for Vision Systems
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
480 pages, Paperback
Published November 10, 2020
27 people are currently reading
103 people want to read
Ratings & Reviews
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Displaying 1 - 8 of 8 reviews
April 6, 2021
Extremely great book! This is the only one I’ve found that has comprehensive summaries for object detection.
March 18, 2022
Excellent book! Clear-cut and easy to read and understand for such an elaborate field. It is obvious that the author really enjoys writing about his subject and successfully takes you through this journey without stuffing you with unnecessary information but at the same time being thorough and technical enough!
July 15, 2025
I read it when working my master degree when using YOLO. It really explain everything good.
February 3, 2022
An Excellent Work in Deep Learning -- technical and practical.
Chapter One: Computer Vision & Applications
Chapter Two: Deep Learning and Neural Networks
Chapter Three: Convolutional Neural Networks
Chapter Four: Deep Learning Projects and Hyper-parameter tuning
Chapter Five: Convolutional Neural Networks Architecture
Chapter Six: Transfer Learning
Chapter Seven: Object Detection
Chapter Eight: Generative Adversarial Networks
Chapter Nine: DeepDream and Neural Style Transfer
Chapter Ten: Visual Embedding
Recommended for Computer Scientists, Researchers & Practitioners
Deus Vult,
Gottfried
Chapter One: Computer Vision & Applications
Chapter Two: Deep Learning and Neural Networks
Chapter Three: Convolutional Neural Networks
Chapter Four: Deep Learning Projects and Hyper-parameter tuning
Chapter Five: Convolutional Neural Networks Architecture
Chapter Six: Transfer Learning
Chapter Seven: Object Detection
Chapter Eight: Generative Adversarial Networks
Chapter Nine: DeepDream and Neural Style Transfer
Chapter Ten: Visual Embedding
Recommended for Computer Scientists, Researchers & Practitioners
Deus Vult,
Gottfried
February 1, 2025
It’s the first ML, deep learning and CV book I’ve read that actually builds up in a way that progresses from the basics to the algorithms. I was totally stuck trying to understand all the fundamentals of ML, and this book finally unlocked it for me! It doesn’t just throw everything at you with math that I can’t even understand yet.
And the pictures and diagrams? Lifesavers <3 I literally only learn if people literally draw things out for me, and this book did just that.
And the pictures and diagrams? Lifesavers <3 I literally only learn if people literally draw things out for me, and this book did just that.
August 18, 2021
Great book if you are a beginner in the field. The concepts are explained very nicely. It has the best explanation of image segmentation of all the book I have read
August 19, 2024
"This book is comprehensive, approachable, and relevant for modern applications of deep learning to computer vision systems." Good for researchers in CV, NN, ...
January 6, 2025
An amazing book for intermediate deep learning enthusiasts. Everything is explained well and the author has such a great writing voice as well.
Displaying 1 - 8 of 8 reviews