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You're reading from Python Deep Learning Projects 9 projects demystifying neural network and deep learning models for building intelligent systems

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781788997096

Length 472 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (3):

Rahul Kumar

Matthew Lamons

Abhishek Nagaraja

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Table of Contents (23) Chapters

Title Page

Dedication

Packt Upsell

Contributors

Preface

1. Building Deep Learning Environments FREE CHAPTER

2. Training NN for Prediction Using Regression

3. Word Representation Using word2vec

4. Building an NLP Pipeline for Building Chatbots

5. Sequence-to-Sequence Models for Building Chatbots

6. Generative Language Model for Content Creation

7. Building Speech Recognition with DeepSpeech2

8. Handwritten Digits Classification Using ConvNets

9. Object Detection Using OpenCV and TensorFlow

10. Building Face Recognition Using FaceNet

11. Automated Image Captioning

12. Pose Estimation on 3D models Using ConvNets

13. Image Translation Using GANs for Style Transfer

14. Develop an Autonomous Agent with Deep R Learning

15. Summary and Next Steps in Your Deep Learning Career

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Index

Chapter 3. Word representation using Word2VEC

Our Python Deep Learning Projects team is doing good work and our (hypothetical) business use case has expanded! In the last project, we were asked to accurately classify handwritten digits to generate a phone number for an available table seating notification text to be sent out to patrons of a restaurant chain. What we learned after the project, was that the text that the restaurant sent out had a message that was friendly and well received. The restaurant was actually getting texts back!

The notification text was: "We're excited that you're here and your table is ready! See the greeter, and we'll seat you and your party now. :) "

Response texts were varied and usually short, but it was noticed by the greeter and the restaurant management and they started thinking that maybe they could use this simple system to get feedback on the dining experience. This feedback would provide needed business intelligence on how the food tasted, how the service...