Description
Deep Learning for Vision Systems 1st Edition
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
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
Table of Contents
1 Welcome to computer vision
2 Deep learning and neural networks
3 Convolutional neural networks
5 Advanced CNN architectures
6 Transfer learning
7 Object detection with R-CNN, SSD, and YOLOPART 3 – GENERATIVE MODELS AND VISUAL EMBEDDINGS
8 Generative adversarial networks (GANs)
9 DeepDream and neural style transfer
10 Visual embeddings
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