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    Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS Emily Webber, Andrea Olgiati

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS Emily Webber, Andrea Olgiati - okladka książki

    Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS Emily Webber, Andrea Olgiati - okladka książki

    Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS Emily Webber, Andrea Olgiati - audiobook MP3

    Pretrain Vision and Large Language Models in Python. End-to-end techniques for building and deploying foundation models on AWS Emily Webber, Andrea Olgiati - audiobook CD

    Autorzy:
    Emily Webber, Andrea Olgiati
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    258
    Dostępne formaty:
         PDF
         ePub
    Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization.

    With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you’ll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models.

    You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines.

    By the end of this book, you’ll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future.

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    O autorze książki

    Emily Webber is a Principal Machine Learning Specialist Solutions Architect at Amazon Web Services. She has assisted hundreds of customers on their journey to ML in the cloud, specializing in distributed training for large language and vision models. She mentors Machine Learning Solution Architects, authors countless feature designs for SageMaker and AWS, and guides the Amazon SageMaker product and engineering teams on best practices in regards around machine learning and customers. Emily is widely known in the AWS community for a 16-video YouTube series featuring SageMaker with 160,000 views, plus a Keynote at O’Reilly AI London 2019 on a novel reinforcement learning approach she developed for public policy.

    Packt Publishing - inne książki

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