Name: Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks (en Inglés)
Price: 81260.00 CLP
Availability: InStock
Author: Palczewski, Tomasz ; Lee, Jaejun ; Mookiah, Lenin
ISBN: 9781803243665

portada Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

322

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 1.7 cm

Peso

0.55 kg.

ISBN13

9781803243665

Categorías

Ingeniería electrónica
Análisis y captura de datos
Visualización de la información

Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks (en Inglés)

Tomasz Palczewski (Autor) · Jaejun Lee (Autor) · Lenin Mookiah (Autor) · Packt Publishing · Tapa Blanda

Libro Físico

$ 81.260

$ 112.860

Ahorras: $ 31.600

28% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks (en Inglés)"

Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud servicesKey Features: Understand how to execute a deep learning project effectively using various tools availableLearn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deploymentBook 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 TensorFlowConvert a proof-of-concept model into a production-ready applicationDiscover how to set up a deep learning pipeline in an efficient way using AWSExplore different ways to compress a model for various deployment requirementsDevelop Android and iOS applications that run deep learning on mobile devicesMonitor a system with a deep learning model in productionChoose the right system architecture for developing and deploying a modelWho 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.