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Deep Learning with TensorFlow 2 and Keras

You're reading from Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

Product type Paperback

Published in Dec 2019

Publisher Packt

ISBN-13 9781838823412

Length 646 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (3):

Dr. Amita Kapoor

Sujit Pal

Antonio Gulli

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 FREE CHAPTER

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

TensorFlow.js

TensorFlow.js is a JavaScript library for machine learning models that can work either in vanilla mode or via Node.js. In this section we are going to review both of them.

Vanilla TensorFlow.js

TensorFlow.js is a JavaScript library for training and using Machine Learning (ML) models in a browser. It is derived from deeplearn.js, an open source, hardware-accelerated library for doing Deep Learning (DL) in JavaScript, and is now a companion library to TensorFlow.

The most common use of TensorFlow.js is to make pretrained ML/DL models available on the browser. This can help in situations where it may not be feasible to send client data back to the server due to network bandwidth or security concerns. However, TensorFlow.js is a full stack ML platform, and it is possible to build and train an ML/DL model from scratch, as well as fine-tune an existing pretrained model with new client data.

An example of a TensorFlow.js application is the TensorFlow Projector (https...

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Authors (3)

Antonio Gulli

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.

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Amita Kapoor

Amita Kapoor

Amita Kapoor, a seasoned expert in Artificial Intelligence, serves as the Chief Artificial Intelligence Officer at TIPZ AI, bringing over 25 years of experience in AI, data science, and neuroscience. Her consultancy, NePeur, stands testament to her leadership in applying AI across diverse industries, enhancing operational efficiency and business intelligence. Amita is also a devoted board member of Neuromatch Academy, where she plays a crucial role in making neuroscience and deep learning education accessible to all. Holding a PhD from the University of Delhi, she has dedicated her career to education, authoring numerous articles and papers, and creating impactful online classes. Her significant contributions extend to pioneering projects in intelligent vehicle fleet management, home surveillance through AI-powered face detection, and robust data anonymization solutions. Connect with Amita on LinkedIn.

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Sujit Pal

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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