Deep Learning for Vision Systems

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