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Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 558 pages
Edition 2nd Edition
Tools
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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 (21) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 20. Index

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, we will...

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