| Description |
1 online resource |
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text file |
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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. |
| 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. |
| Subject |
TensorFlow.
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Neural networks (Computer science)
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Data sets.
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Neural Networks, Computer |
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Datasets as Topic |
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Réseaux neuronaux (Informatique) |
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Jeux de données. |
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Data sets |
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Neural networks (Computer science) |
| Genre |
Dataset |
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Data sets
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Data sets.
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Jeux de données.
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| Other Form: |
Print version: 1484261674 9781484261675 (OCoLC)1159046360 |
| ISBN |
9781484261682 (electronic bk.) |
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1484261682 (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-6168-2 doi |
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