Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Intelligent Mobile Projects with TensorFlow
  • Toc
  • feedback
Intelligent Mobile Projects with TensorFlow

Intelligent Mobile Projects with TensorFlow

By : Tang
5 (4)
close
Intelligent Mobile Projects with TensorFlow

Intelligent Mobile Projects with TensorFlow

5 (4)
By: Tang

Overview of this book

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.
Table of Contents (14 chapters)
close

Neural Style Transfer – a quick overview

The original idea and algorithm of using a deep neural network to merge the content of an image with the style of another was published in a paper titled A Neural Algorithm of Artistic Style (https://arxiv.org/abs/1508.06576) in the summer of 2015. It was based on a pre-trained deep CNN model called VGG-19 (https://arxiv.org/pdf/1409.1556.pdf), the winner of the 2014 ImageNet image recognition challenge, which has 16 convolutional layers, or feature maps, representing different levels of the image content. In this original method, the final transferred image is first initialized as a white noise image merged with the content image. The content loss function is defined as the squared error loss of a specific set of feature representations on the convolutional layer, conv4_2, of the content image and the result image after both being...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete