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Convolutional neural networks with S...

Koonce, Brett.

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Convolutional neural networks with Swift for Tensorflow = image recognition and dataset categorization /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Convolutional neural networks with Swift for Tensorflow/ by Brett Koonce.
Reminder of title:	image recognition and dataset categorization /
Author:	Koonce, Brett.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xxi, 245 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-1-4842-6168-2
ISBN:	9781484261682

Convolutional neural networks with Swift for Tensorflow = image recognition and dataset categorization /
Koonce, Brett.

Convolutional neural networks with Swift for Tensorflowimage recognition and dataset categorization /[electronic resource] :by Brett Koonce. - Berkeley, CA :Apress :2021. - xxi, 245 p. :ill., digital ;24 cm.

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.

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. You will: Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices.

ISBN: 9781484261682

Standard No.: 10.1007/978-1-4842-6168-2doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .K666 2021

Dewey Class. No.: 006.32

Convolutional neural networks with Swift for Tensorflow = image recognition and dataset categorization /
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505 0 $a 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.
520 $a 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. You will: Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices.
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