rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Deep learning with PyTorch Lightning
    sub title: build and train high-performance artificial intelligence and self-supervised models using Python
  name:
    name part: Sawarkar, Kunal
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Arremsetty, Dheeraj
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: enk
    date issued: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : color illustrations
  abstract: Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key Features Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains Speed up your research using PyTorch Lightning by creating new loss functions, networks, and architectures Train and build new algorithms for massive data using distributed training Book Description PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging. By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning. What you will learn Customize models that are built for different datasets, model architectures, and optimizers Understand how a variety of Deep Learning models from image recogni…
  note: Kunal Sawarkar, Dheeraj Arremsetty.
  subject:
    topic: Machine learning
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Machine learning
  classification: Q325.5
  classification: 006.31
  identifier: 1800569270
  identifier: 9781800569270
  identifier: 9781800561618 (pbk.)
  identifier: 9781800569270 Packt Publishing Pvt. Ltd
  identifier: 9781800561618 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: UKMGB
    record creation date: 210803
    record change date: 20260130165655.6
    record identifier: in00004670587
    language of cataloging:
      language term: eng