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Python Deep Learning Projects

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

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788997096
Length 472 pages
Edition 1st Edition
Languages
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Authors (3):
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Rahul Kumar Rahul Kumar
Author Profile Icon Rahul Kumar
Rahul Kumar
Matthew Lamons Matthew Lamons
Author Profile Icon Matthew Lamons
Matthew Lamons
Abhishek Nagaraja Abhishek Nagaraja
Author Profile Icon Abhishek Nagaraja
Abhishek Nagaraja
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Toc

Table of Contents (17) Chapters Close

Preface 1. Building Deep Learning Environments 2. Training NN for Prediction Using Regression FREE CHAPTER 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 16. Other Books You May Enjoy

Generating lyrics using deep (multi-layer) LSTM

Now that we have built a basic LSTM model for text generation and learned its value, let's move one step further and create a deep LSTM model suited for the task of generating music lyrics. We now have a new goal: to build and train a model that outputs entirely new and original lyrics that is in the style of an arbitrary number of artists.

Let's begin. You can refer to the code file found at Lyrics-ai (https://github.com/PacktPublishing/Python-Deep-Learning-Projects/tree/master/Chapter06/Lyrics-ai) for this exercise.

Data pre-processing

To build a model that can generate lyrics, we will need a huge amount of lyric data, which can easily be extracted from various sources...

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