Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization

ISBN-13: 9781484261675 / Angielski / Miękka / 2021 / 245 str.

Brett Koonce

Spis treści

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

O autorze

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.

Blurb

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