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Mastering PyTorch

You're reading from   Mastering PyTorch Build powerful neural network architectures using advanced PyTorch 1.x features

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789614381
Length 450 pages
Edition 1st Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (20) Chapters Close

Preface 1. Section 1: PyTorch Overview
2. Chapter 1: Overview of Deep Learning using PyTorch FREE CHAPTER 3. Chapter 2: Combining CNNs and LSTMs 4. Section 2: Working with Advanced Neural Network Architectures
5. Chapter 3: Deep CNN Architectures 6. Chapter 4: Deep Recurrent Model Architectures 7. Chapter 5: Hybrid Advanced Models 8. Section 3: Generative Models and Deep Reinforcement Learning
9. Chapter 6: Music and Text Generation with PyTorch 10. Chapter 7: Neural Style Transfer 11. Chapter 8: Deep Convolutional GANs 12. Chapter 9: Deep Reinforcement Learning 13. Section 4: PyTorch in Production Systems
14. Chapter 10: Operationalizing PyTorch Models into Production 15. Chapter 11: Distributed Training 16. Chapter 12: PyTorch and AutoML 17. Chapter 13: PyTorch and Explainable AI 18. Chapter 14: Rapid Prototyping with PyTorch 19. Other Books You May Enjoy

Chapter 6: Music and Text Generation with PyTorch

PyTorch is a fantastic tool for both researching deep learning models and developing deep learning-based applications. In the previous chapters, we looked at model architectures across various domains and model types. We used PyTorch to build these architectures from scratch and used pre-trained models from the PyTorch model zoo. We will switch gears from this chapter onward and dive deep into generative models.

In the previous chapters, most of our examples and exercises revolved around developing models for classification, which is a supervised learning task. However, deep learning models have also proven extremely effective when it comes to unsupervised learning tasks. Deep generative models are one such example. These models are trained using lots of unlabeled data. Once trained, the model can generate similar meaningful data. It does so by learning the underlying structure and patterns in the input data.

In this chapter,...

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