Mastering PyTorch - Second Edition
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Mastering PyTorch - Second Edition

Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

About the Book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models.

You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.

By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

About the Author

Packt Publishing Ltd
Packt Publishing Ltd

Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com

Table of Contents

  1. Overview of Deep Learning using PyTorch
  2. Deep CNN architectures
  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 TorchRec
  19. PyTorch and Hugging Face

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