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...

Intelligent Mobile Projects with TensorFlow
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Intelligent Mobile Projects with TensorFlow
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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)
Preface
Getting Started with Mobile TensorFlow
Classifying Images with Transfer Learning
Detecting Objects and Their Locations
Transforming Pictures with Amazing Art Styles
Understanding Simple Speech Commands
Describing Images in Natural Language
Recognizing Drawing with CNN and LSTM
Predicting Stock Price with RNN
Generating and Enhancing Images with GAN
Building an AlphaZero-like Mobile Game App
Using TensorFlow Lite and Core ML on Mobile
Developing TensorFlow Apps on Raspberry Pi
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