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Mastering PyTorch

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4.7 (20)
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Mastering PyTorch

Mastering PyTorch

4.7 (20)
By: Ashish Ranjan Jha

Overview of this 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.
Table of Contents (22 chapters)
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Index

Mastering Pytorch, Second Edition: Build powerful deep learning architectures using advanced PyTorch features

Welcome to Packt Early Access. We’re giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time.

You can dip in and out of this book or follow along from start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.

  1. Chapter 1: Overview of Deep Learning with PyTorch
  2. Chapter 2: Combining CNNs and LSTMs
  3. Chapter 3: Deep CNN architectures
  4. Chapter 4: Deep Recurrent Model Architectures
  5. Chapter 5: Hybrid Advanced Neural Networks
  6. Chapter 6: Graph Neural...
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Mastering PyTorch
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