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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

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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 6. Generative Language modeling for content creation

This work is certainly getting exciting and the word is out that we're demonstrating a professional set of deep learning capabilities in producing solutions for a wide range of business use cases! As data scientists, we understand the transferability of our skills. We know we can provide value by employing core skills when working on problems that we know are similar in structure but that may seem different at first glance. This couldn't be more true than in the next Deep Learning Project where we're (hypothetically) going to be working on in which a creative group has asked us to help to produce original content for movie scripts, song lyrics and even music!

How can we leverage our experience in solving problems for restaurant chains to such a different industry? Let's explore what we know and what we're going to be asked to do. In past projects we demonstrated that we could take an image as input and output a class label (Chapter...