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Emily Webber
Pretrain Vision and Large Language Models in Python
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815011Pretrain Vision and Large Language Models in Pythonhttps://www.gandhi.com.mx/pretrain-large-vision-and-language-models-for-beginners/phttps://gandhi.vtexassets.com/arquivos/ids/1022310/adb651db-3f1a-4449-9331-45567b7ac3c0.jpg?v=638337211390670000739821MXNPackt PublishingInStock/Ebooks/812197Pretrain Vision and Large Language Models in Python739821https://www.gandhi.com.mx/pretrain-large-vision-and-language-models-for-beginners/phttps://gandhi.vtexassets.com/arquivos/ids/1022310/adb651db-3f1a-4449-9331-45567b7ac3c0.jpg?v=638337211390670000InStockMXN99999DIEbook20239781804612545_W3siaWQiOiI4NjQ5OTUxMS0zNjFmLTQ5YzctYmRmYi01OWQ1YjU4Mzg4YzMiLCJsaXN0UHJpY2UiOjgyMSwiZGlzY291bnQiOjgyLCJzZWxsaW5nUHJpY2UiOjczOSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781804612545_<p><strong>Conceptual fundamentals and practical guidance from industry experts to pretrain the large vision and language models of the future.</strong></p><h4>Key Features</h4><ul><li>Learn how and where to develop, train, tune, and apply your own pretrained models</li><li>Master distributed training concepts for models & datasets, with code examples for AWS and SageMaker</li><li>Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring</li></ul><h4>Book Description</h4><p>Large models have forever changed machine learning. From BERT to GPT-3, Vision Transformers to DALL-E, 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 your large models from scratch on AWS and Amazon SageMaker and apply them to hundreds of use cases across your organization.</p><p>With advice from seasoned AWS ML expert Emily Webber, this book provides everything you need to go from project ideation, 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 all the way from mastering the concept of pretraining itself to preparing your dataset and model, configuring your environment, training, evaluating, and deploying your models.</p><p>From applying the scaling laws to distributing your model and dataset over multiple GPUs, youll learn how to successfully train, evaluate, and deploy your model on Amazon SageMaker. By the end of this book, you will have everything you need to embark on your own project to pretrain the large language models of the future, purpose-built for your organization.</p><h4>What you will learn</h4><ul><li>Prepare to train large models from the right dataset to your GPU needs</li><li>Configure environments on AWS and SageMaker for optimal performance</li><li>Select the right hyperparameters for your model, given your constraints</li><li>Distribute your model and dataset with different types of parallelism</li><li>Avoid pitfalls with job restarts, intermittent health checks, and more</li><li>Evaluate your model with quantitative and qualitative insights</li><li>Deploy your models with runtime improvements and Monitoring</li><li>Detect and mitigate bias in your deploy and retrain pipelines</li></ul><h4>Who This Book Is For</h4><p>If youre a machine learning enthusiast or researcher who wants to get started on your very own large modeling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all enjoy the material. Basic Python is a must, and introductory concepts around cloud computing will be very helpful. Well assume some level of deep learning fundamentals but will explain advanced topics.</p><h4>Table of Contents</h4><ol><li>An introduction to pretraining</li><li>Dataset preparation: part one</li><li>Model preparation</li><li>Into the GPU</li><li>Parallelization basics</li><li>Dataset preparation: part two</li><li>Find the right hyperparameters</li><li>Make sure your loss goes down</li><li>Troubleshoot ongoing performance</li><li>Determine the right length of training time</li><li>Finetune and compare with open source models</li><li>Detect and mitigate bias</li><li>How small can you go?</li><li>Use cases: scale across organizations</li><li>Ongoing operations, monitoring and maintenance</li></ol>...(*_*)9781804612545_<p><b>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 examples</b></p><h4>Key Features</h4><ul><li>Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines</li><li>Explore large-scale distributed training for models and datasets with AWS and SageMaker examples</li><li>Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring</li></ul><h4>Book Description</h4>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, 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.<h4>What you will learn</h4><ul><li>Find the right use cases and datasets for pretraining and fine-tuning</li><li>Prepare for large-scale training with custom accelerators and GPUs</li><li>Configure environments on AWS and SageMaker to maximize performance</li><li>Select hyperparameters based on your model and constraints</li><li>Distribute your model and dataset using many types of parallelism</li><li>Avoid pitfalls with job restarts, intermittent health checks, and more</li><li>Evaluate your model with quantitative and qualitative insights</li><li>Deploy your models with runtime improvements and monitoring pipelines</li></ul><h4>Who this book is for</h4><p>If 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.</p>...9781804612545_Packt Publishinglibro_electonico_7dc3527b-b0ac-344d-8dc6-21e48296b9b8_9781804612545;9781804612545_9781804612545Emily WebberInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-cfb2b7b5-cf42-4d91-b9bd-d85097c6983a.epub2023-05-31T00:00:00+00:00Packt Publishing