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Neural Network Programming with TensorFlow

You're reading from Neural Network Programming with TensorFlow Unleash the power of TensorFlow to train efficient neural networks

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781788390392

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Neural Networks

Authors (2):

Manpreet Singh Ghotra

Rajdeep Dua

View More author details

Table of Contents (11) Chapters

Preface

1. Maths for Neural Networks

2. Deep Feedforward Networks FREE CHAPTER

3. Optimization for Neural Networks

4. Convolutional Neural Networks

5. Recurrent Neural Networks

6. Generative Models

7. Deep Belief Networking

8. Autoencoders

9. Research in Neural Networks

10. Getting started with TensorFlow

Summary

In this chapter, you learned three different types of autoencoders: basic, Additive Gaussian Noise, and Sparse. We understood the use cases where they can be useful. We ran them against the MNIST dataset and also compared the cost of the three autoencoders. We also plotted the weights as well as the approximate output.

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Authors (2)

Dua

Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

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Ghotra

Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.

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