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Deep Learning with PyTorch Lightning

Deep Learning with PyTorch Lightning

By : Kunal Sawarkar, Dheeraj Arremsetty
4.3 (16)
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Deep Learning with PyTorch Lightning

Deep Learning with PyTorch Lightning

4.3 (16)
By: Kunal Sawarkar, Dheeraj Arremsetty

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)
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1
Section 1: Kickstarting with PyTorch Lightning
6
Section 2: Solving using PyTorch Lightning
11
Section 3: Advanced Topics

Chapter 3: Transfer Learning Using Pre-Trained Models

Deep learning models become more accurate the more data they have for training. The most spectacular Deep Learning models, such as ImageNet, are trained on millions of images and often require a massive amount of computing power. To put things into perspective, the amount of power used to train OpenAI's GPT3 model could power an entire city. Unsurprisingly, the cost of training such Deep Learning models from scratch is prohibitive for most projects.

This begs the question: do we really need to train a Deep Learning model from scratch each time? One way of getting around this problem, rather than training Deep Learning models from scratch, is to borrow representations from an already trained model for a similar subject. For example, if you wanted to train an image recognition model to detect faces, you could train your Convolutional Neural Network (CNN) to learn all the representations for each of the layers – or...

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