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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 2: Getting off the Ground with the First Deep Learning Model

Deep learning (DL) models have gained tremendous popularity in recent times and have caught the attention of data scientists in academia and industry alike. The reason behind their great success is their ability to solve the simplest yet oldest problem in computer science—computer vision. It had long been the dream of computer scientists to find an algorithm that would make machines see like humans do… or at least be able to recognize objects. DL models power not just object recognition but are used in everything, from predicting who is in an image, to natural language processing (NLP)—where they can be used for predicting and generating text and understanding speech—and even creating deepfakes, such as videos. At their core, all DL models are built using neural network (NN) algorithms; however, they are much more than just a NN. While NNs have been used since the 1950s, it's only...

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