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

Deep CNN Architectures

In this chapter, we will first briefly review the evolution of Convolutional Neural Network (CNN) architectures, and then we will study the different CNN architectures in detail. We will implement these CNN architectures using PyTorch, and in doing so, we aim to exhaustively explore the tools (modules and built-in functions) that PyTorch has to offer in the context of building Deep CNNs. Gaining strong CNN expertise in PyTorch will enable us to solve a number of deep learning problems involving CNNs. This will also help us in building more complex deep learning models or applications of which CNNs are a part.

This chapter will cover the following topics:

  • Why are CNNs so powerful?
  • Evolution of CNN architectures
  • Developing LeNet from scratch
  • Fine-tuning the AlexNet model
  • Running a pretrained VGG model
  • Exploring GoogLeNet and Inception v3
  • Discussing ResNet and DenseNet architectures
  • Understanding EfficientNets and the future of CNN architectures

All the code files for this chapter can be found at https://github.com/arj7192/MasteringPyTorchV2/tree/main/Chapter03.

Let us start by discussing the key features of CNNs.