Pretrain Vision and Large Language Models in Python: End-to-end techniques for b

Description: Pretrain Vision and Large Language Models in Python by Emily Webber, Andrea Olgiati Estimated delivery 3-12 business days Format Paperback Condition Brand New Description Foundation models are the future of machine learning and the backbone of Generative AI. Pretrain Vision and Large Language Models in Python provides you with the conceptual fundamentals, industry-proven best practices, and code snippets you need to help you get started on your large modeling projects at work, in research labs, or just for fun. Publisher Description Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examplesKey FeaturesLearn to develop, train, tune, and apply foundation models with optimized end-to-end pipelinesExplore large-scale distributed training for models and datasets with AWS and SageMaker examplesEvaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoringBook DescriptionFoundation 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, youll 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, youll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future.What you will learnFind the right use cases and datasets for pretraining and fine-tuningPrepare for large-scale training with custom accelerators and GPUsConfigure environments on AWS and SageMaker to maximize performanceSelect hyperparameters based on your model and constraintsDistribute your model and dataset using many types of parallelismAvoid pitfalls with job restarts, intermittent health checks, and moreEvaluate your model with quantitative and qualitative insightsDeploy your models with runtime improvements and monitoring pipelinesWho this book is forIf youre a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way. Author Biography 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 OReilly AI London 2019 on a novel reinforcement learning approach she developed for public policy. Details ISBN 180461825X ISBN-13 9781804618257 Title Pretrain Vision and Large Language Models in Python Author Emily Webber, Andrea Olgiati Format Paperback Year 2023 Pages 258 Publisher Packt Publishing Limited GE_Item_ID:142453031; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. 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