Convolutional neural networks with Swift for TensorFlow : image recognition and dataset categorization /

Convolutional neural networks with Swift for TensorFlow : image recognition and dataset categorization /

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Bibliographic Details
Author / Creator:	Koonce, Brett.
Imprint:	[United States] : Apress, 2021.
Description:	1 online resource
Language:	English
Subject:	TensorFlow. Neural networks (Computer science) Data sets. Neural Networks, Computer Datasets as Topic Data sets Neural networks (Computer science) Dataset Data sets Data sets.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13686723

Hidden Bibliographic Details
ISBN:	9781484261682 1484261682 1484261674 9781484261675
Digital file characteristics:	text file PDF
Summary:	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.
Other form:	Print version: 1484261674 9781484261675
Standard no.:	10.1007/978-1-4842-6168-2

Table of Contents:

Chapter 1: MNIST: 1D Neural Network
Chapter 2: MNIST: 2D Neural Network
Chapter 3: CIFAR: 2D Nueral Network with Blocks
Chapter 4: VGG Network
Chapter 5: Resnet 34
Chapter 6: Resnet 50
Chapter 7: SqueezeNet
Chapter 8: MobileNrt v1
Chapter 9: MobileNet v2
Chapter 10: Evolutionary Strategies
Chapter 11: MobileNet v3
Chapter 12: Bag of Tricks
Chapter 13: MNIST Revisited
Chapter 14: You are Here.