Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
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 (21 chapters)
20
Index

Exploring GoogLeNet and Inception v3

As we have discovered the progression of CNN models from LeNet to VGG so far, we have observed the sequential stacking of more convolutional and fully connected layers. This resulted in deep networks with a lot of parameters to train. GoogLeNet emerged as a radically different type of CNN architecture that is composed of a module of parallel convolutional layers called the inception module. Because of this, GoogLeNet is also called Inception v1 (v1 marked the first version as more versions came along later). Some of the drastically new elements introduced in GoogLeNet were the following:

  • The inception module – a module of several parallel convolutional layers
  • Using 1x1 convolutions to reduce the number of model parameters
  • Global average pooling instead of a fully connected layer – reduces overfitting
  • Using auxiliary classifiers for training – for regularization and gradient stability

GoogLeNet has 22 layers, which is more than...