Book Image

Mastering PyTorch

By : Ashish Ranjan Jha
Book Image

Mastering PyTorch

By: Ashish Ranjan Jha

Overview of this book

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (20 chapters)
1
Section 1: PyTorch Overview
4
Section 2: Working with Advanced Neural Network Architectures
8
Section 3: Generative Models and Deep Reinforcement Learning
13
Section 4: PyTorch in Production Systems

Chapter 3: Deep CNN Architectures

In this chapter, we will first briefly review the evolution of CNNs (in terms of 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. Building 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 pre-trained VGG model
  • Exploring GoogLeNet and Inception v3
  • Discussing ResNet and DenseNet architectures
  • Understanding EfficientNets and the future of...