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

Developing LeNet from scratch

LeNet, originally known as LeNet-5, is one of the earliest CNN models, developed in 1998. The number 5 in LeNet-5 represents the total number of layers in this model, that is, two convolutional and three fully connected layers. With roughly 60,000 total parameters, this model gave state-of-the-art performance on image recognition tasks for handwritten digit images in the year 1998. As expected from a CNN model, LeNet demonstrated rotation, position, and scale invariance as well as robustness against distortion in images. Contrary to the classical machine learning models of the time, such as SVMs, which treated each pixel of the image separately, LeNet exploited the correlation among neighboring pixels.

Note that although LeNet was developed for handwritten digit recognition, it can certainly be extended for other image classification tasks, as we shall see in our next exercise. The following diagram shows the architecture of a LeNet model:

...