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PRODUCTION-READY APPLIED DEEP LEARNING learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks /
Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services Key Features Understand how to execute a deep learning project effectively using various tools available Learn how to develop PyTorch and TensorFlow models at scale us...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[S.l.] :
PACKT PUBLISHING LIMITED,
2022.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Palczewski, Tomasz. | |
| 245 | 1 | 0 | |a PRODUCTION-READY APPLIED DEEP LEARNING |h [electronic resource] : |b learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks / |c Tomasz Palczewski, Jaejun (Brandon) Lee, Lenin Mookiah. |
| 260 | |a [S.l.] : |b PACKT PUBLISHING LIMITED, |c 2022. | ||
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| 520 | |a Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services Key Features Understand how to execute a deep learning project effectively using various tools available Learn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deployment Book Description Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives. First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale. By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting. What you will learn Understand how to develop a deep learning model using PyTorch and TensorFlow Convert a proof-of-concept model into a production-ready application Discover how to set up a deep learning pipeline in an efficient way using AWS Explore different ways to compress a model for various deployment requirements Develop Android and iOS applications that run deep learning on mobile devices Monitor a system with a deep learning model in production Choose the right system architecture for developing and deploying a model Who this book is for Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily. | ||
| 505 | 0 | |a Table of Contents Effective Planning of Deep Learning-Driven Projects Data Preparation for Deep Learning Projects Developing a Powerful Deep Learning Model Experiment Tracking, Model Management, and Dataset Versioning Data Preparation in the Cloud Efficient Model Training Revealing the Secret of Deep Learning Models Simplifying Deep Learning Model Deployment Scaling a Deep Learning Pipeline Improving Inference Efficiency Deep Learning on Mobile Devices Monitoring Deep Learning Endpoints in Production Reviewing the Completed Deep Learning Project. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a TensorFlow. |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Application program interfaces (Computer software) | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Natural language processing (Computer science) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Interfaces de programmation d'applications. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
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| 650 | 7 | |a Application program interfaces (Computer software) |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Natural language processing (Computer science) |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a Lee, Jaejun. | |
| 700 | 1 | |a Mookiah, Lenin. | |
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