Overview
- Task convolutional neural networks for image recognition
- Apply Swift for Tensorflow throughout in order to learn the new framework by example
- Hone the skills needed to tackle problems in the fields of machine learning and deep learning
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Table of contents (14 chapters)
Keywords
About this book
Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language.
It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You’ll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet.
Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field.
What You'll Learn
- Categorize and augment datasets
- Build and train large networks, including via cloud solutions
- Deploy complex systems to mobile devices
Who This Book Is For
Developers with Swift programming experience who would like to learn convolutional neural networks by example using Swift for Tensorflow as a starting point.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Convolutional Neural Networks with Swift for Tensorflow
Book Subtitle: Image Recognition and Dataset Categorization
Authors: Brett Koonce
DOI: https://doi.org/10.1007/978-1-4842-6168-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Brett Koonce 2021
Softcover ISBN: 978-1-4842-6167-5Published: 05 January 2021
eBook ISBN: 978-1-4842-6168-2Published: 04 January 2021
Edition Number: 1
Number of Pages: XXI, 245
Number of Illustrations: 1 b/w illustrations
Topics: Apple and iOS, Machine Learning