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Deep Learning with TensorFlow 2 and Keras - Second Edition

You're reading from Deep Learning with TensorFlow 2 and Keras - Second Edition

Product type Book

Published in Dec 2019

Publisher Packt

ISBN-13 9781838823412

Pages 646 pages

Edition 2nd Edition

Languages

Concepts

Authors (3):

Antonio Gulli

Amita Kapoor

Sujit Pal

View More author details

Table of Contents (19) Chapters

Preface

1. Neural Network Foundations with TensorFlow 2.0

2. TensorFlow 1.x and 2.x

3. Regression

4. Convolutional Neural Networks

5. Advanced Convolutional Neural Networks

6. Generative Adversarial Networks

7. Word Embeddings

8. Recurrent Neural Networks

9. Autoencoders

10. Unsupervised Learning

11. Reinforcement Learning

12. TensorFlow and Cloud

13. TensorFlow for Mobile and IoT and TensorFlow.js

14. An introduction to AutoML

15. The Math Behind Deep Learning

16. Tensor Processing Unit

17. Other Books You May Enjoy

18. Index

Very deep convolutional networks for large-scale image recognition

During 2014, an interesting contribution to image recognition was presented with the paper, Very Deep Convolutional Networks for Large-Scale Image Recognition, K. Simonyan and A. Zisserman [4]. The paper showed 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 had 16 deep layers.

An implementation in Java Caffe (http://caffe.berkeleyvision.org/) was 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 (224×224) on 3 channels. The model achieves 7.5% top-5 error on ILSVRC-2012-val, 7.4% top-5 error on ILSVRC-2012-test.

According to the ImageNet site...

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Authors (3)

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

Amita Kapoor

Amita Kapoor is an accomplished AI consultant and educator, with over 25 years of experience. She has received international recognition for her work, including the DAAD fellowship and the Intel Developer Mesh AI Innovator Award. She is a highly respected scholar in her field, with over 100 research papers and several best-selling books on deep learning and AI. After teaching for 25 years at the University of Delhi, Amita took early retirement and turned her focus to democratizing AI education. She currently serves as a member of the Board of Directors for the non-profit Neuromatch Academy, fostering greater accessibility to knowledge and resources in the field. Following her retirement, Amita also founded NePeur, a company that provides data analytics and AI consultancy services. In addition, she shares her expertise with a global audience by teaching online classes on data science and AI at the University of Oxford.

Read more

See other products by Amita Kapoor

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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