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TensorFlow 1.x Deep Learning Cookbook

You're reading from   TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Toc

Table of Contents (15) Chapters Close

Preface 1. TensorFlow - An Introduction FREE CHAPTER 2. Regression 3. Neural Networks - Perceptron 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Recurrent Neural Networks 7. Unsupervised Learning 8. Autoencoders 9. Reinforcement Learning 10. Mobile Computation 11. Generative Models and CapsNet 12. Distributed TensorFlow and Cloud Deep Learning 13. Learning to Learn with AutoML (Meta-Learning) 14. TensorFlow Processing Units

Classifying images with VGGNet, ResNet, Inception, and Xception

Image classification is a typical deep learning application. This task had an initial increase of interest thanks to the ImageNet (http://image-net.org/) image database organized according to the WordNet (http://wordnet.princeton.edu/) hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. More precisely, ImageNet aimed to label and categorize images into almost 22,000 separate object categories. In the context of deep learning, ImageNet refers generally to the work contained in the paper ImageNet Large Scale Visual Recognition Challenge (http://www.image-net.org/challenges/LSVRC/), or ILSVRC for short. In this case, the goal is to train a model that can classify an input image into 1,000 separate object categories. In this recipe, we will use pre...

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