Computers & Internet Books:

Convolutional Neural Networks with Swift for Tensorflow

Image Recognition and Dataset Categorization
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Paperback / softback
$95.99
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Description

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.

Author Biography:

Brett Koonce is the CTO of Quarkworks, a mobile consulting agency.  He's a developer with five years experience creating apps for iOS and Android. His team has worked on dozens of apps that are used by millions of people around the world. Brett knows the pitfalls of development and can help you avoid them. Whether you want to build something from scratch, port your app from iOS to Android (or vice versa) or accelerate your velocity, Brett can help.
Release date Australia
January 5th, 2021
Author
Pages
245
Edition
1st ed.
Audience
  • Professional & Vocational
Illustrations
1 Illustrations, black and white; XXI, 245 p. 1 illus.
ISBN-13
9781484261675
Product ID
33567529

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