TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges
Author:   Jesus Martinez
Publisher:   Packt Publishing Limited
ISBN:  

9781838829131


Pages:   542
Publication Date:   15 January 2021
Format:   Paperback
Availability:   Available To Order   Availability explained
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TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges


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Author:   Jesus Martinez
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
ISBN:  

9781838829131


ISBN 10:   183882913
Pages:   542
Publication Date:   15 January 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

Table of Contents Getting Started with TensorFlow 2.x for Computer Vision Performing Image Classification Harnessing the Power of Pre-Trained Networks with Transfer Learning Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution Reducing Noise with Autoencoders Generative Models and Adversarial Attacks Captioning Images with CNNs and RNNs Fine-Grained Understanding of Images through Segmentation Localizing Elements in Images with Object Detection Applying the Power of Deep Learning to Videos Streamlining Network Implementation with AutoML Boosting Performance

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Author Information

Jesus Martinez is the founder of the computer vision e-learning site DataSmarts. He is a computer vision expert and has worked on a wide range of projects in the field, such as a piece of people-counting software fed with images coming from an RGB camera and a depth sensor, using OpenCV and TensorFlow. He developed a self-driving car in a simulation, using a convolutional neural network created with TensorFlow, that worked solely with visual inputs. Also, he implemented a pipeline that uses several advanced computer vision techniques to track lane lines on the road, as well as providing extra information such as curvature degree.

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