Book Image

Deep Learning with PyTorch Lightning

By : Kunal Sawarkar
3.5 (2)
Book Image

Deep Learning with PyTorch Lightning

3.5 (2)
By: Kunal Sawarkar

Overview of this book

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming. Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation. Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning. By the end of this book, you’ll be able to build and deploy DL models with confidence.
Table of Contents (15 chapters)
1
Section 1: Kickstarting with PyTorch Lightning
6
Section 2: Solving using PyTorch Lightning
11
Section 3: Advanced Topics

Getting started with Self-Supervised Learning

The future of Machine Learning has been hotly contested given the spectacular success of Deep Learning methods such as CNN and RNN in recent years. While CNNs can do amazing things, such as image recognition, and RNNs can generate text, and other advanced NLP methods, such as the Transformer, can achieve marvelous results, all of them have serious limitations when compared to human intelligence. They don't compare very well to humans on tasks such as reasoning, deduction, and comprehension. Also, most notably, they require an enormous amount of well-labeled training data to learn even something as simple as image recognition.

Figure 8.2 – A child learns to classify objects with very few labels

Unsurprisingly, that is not the way humans learn. A child does not need millions of labeled images as input before it can recognize objects. The incredible ability of the human brain to generate its own new labels...