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Deep Learning with Keras

You're reading from Deep Learning with Keras

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787128422

Pages 318 pages

Edition 1st Edition

Languages

Concepts

Authors (2):

Antonio Gulli

Sujit Pal

View More author details

Table of Contents (16) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Neural Networks Foundations

Keras Installation and API

Deep Learning with ConvNets

Generative Adversarial Networks and WaveNet

Word Embeddings

Recurrent Neural Network — RNN

Additional Deep Learning Models

AI Game Playing

Conclusion

Keras 2.0 — what is new

Very deep convolutional networks for large-scale image recognition

In 2014, an interesting contribution for image recognition was presented (for more information refer to: Very Deep Convolutional Networks for Large-Scale Image Recognition, by K. Simonyan and A. Zisserman, 2014). The paper shows that, a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. One model in the paper denoted as D or VGG-16 has 16 deep layers. An implementation in Java Caffe (http://caffe.berkeleyvision.org/) has been used for training the model on the ImageNet ILSVRC-2012 (http://image-net.org/challenges/LSVRC/2012/) dataset, which includes images of 1,000 classes and is split into three sets: training (1.3 million images), validation (50,000 images), and testing (100,000 images). Each image is (224 x 224) on three channels. The model achieves 7.5% top 5 error on ILSVRC-2012-val and 7.4% top 5 error on ILSVRC-2012-test.

According to the ImageNet site...

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Authors (2)

Antonio Gulli

Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.

See other products by Antonio Gulli

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.

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