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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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20
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Index

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In the previous two chapters, we learned extensively about the various convolutional and recurrent network architectures available, along with their implementations in PyTorch. In this chapter, we will take a look at some other deep learning model architectures that have proven to be successful on various machine learning tasks and are neither purely convolutional nor recurrent in nature. We will continue from where we left off in both Chapter 3, Deep CNN Architectures, and Chapter 4, Deep Recurrent Model Architectures.

First, we will explore transformers, which, as we learnt toward the end of Chapter 4, Deep Recurrent Model Architectures, have outperformed recurrent architectures on various sequential tasks. Then, we will pick up from the EfficientNets discussion at the end of Chapter 3, Deep CNN Architectures, and explore the idea of generating randomly wired neural networks, also known as RandWireNNs.

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