Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks
暫譯: 生產就緒的應用深度學習：學習如何在 PyTorch 和 TensorFlow 深度學習框架中構建和部署複雜模型

Palczewski, Tomasz, Lee, Jaejun, Mookiah, Lenin

Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks
  • 出版商: Packt Publishing
  • 出版日期: 2022-08-30
  • 售價: $2,120
  • 貴賓價: 9.5 折 $2,014
  • 語言: 英文
  • 頁數: 322
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 180324366X
  • ISBN-13: 9781803243665
  • 相關分類: DeepLearning、TensorFlow

    • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

提升您的技能，以高效地使用雲端服務開發強大的深度學習模型並進行大規模分發

主要特點：


  • 了解如何有效執行深度學習專案，使用各種可用的工具

  • 學習如何使用 Amazon Web Services 大規模開發 PyTorch 和 TensorFlow 模型

  • 探索針對模型部署中出現的各種困難的有效解決方案

書籍描述：

機器學習工程師、深度學習專家和數據工程師在將深度學習模型移至生產環境時會遇到各種問題。本書的主要目標是通過提供如何轉換各種模型以進行部署的詳細解釋，並有效分發它們，來縮小理論與應用之間的差距。

首先，您將學習如何在 PyTorch 和 TensorFlow 中構建複雜的深度學習模型。接下來，您將獲得將模型從一個框架轉換到另一個框架所需的知識，並學習如何根據部署環境所引入的特定要求進行調整。本書還提供具體的實現和相關的方法論，幫助您立即應用所學知識。您將獲得使用常用深度學習框架和設計用於大規模數據分析的流行雲端服務的實踐經驗。此外，您將掌握作者在大規模部署數百個基於 AI 的服務方面的集體知識。

在本書結束時，您將了解如何將為概念驗證開發的模型轉換為針對特定生產環境優化的生產就緒應用程式。

您將學到的內容：


  • 了解如何使用 PyTorch 和 TensorFlow 開發深度學習模型

  • 將概念驗證模型轉換為生產就緒的應用程式

  • 發現如何使用 AWS 以高效的方式設置深度學習管道

  • 探索針對各種部署要求壓縮模型的不同方法

  • 開發在移動設備上運行深度學習的 Android 和 iOS 應用程式

  • 監控生產中使用深度學習模型的系統

  • 選擇適合開發和部署模型的系統架構

本書適合誰：

機器學習工程師、深度學習專家和數據科學家將發現本書在縮小理論與應用之間的差距方面非常有幫助，並提供詳細的範例。具備初級的機器學習或軟體工程知識將幫助您輕鬆掌握本書所涵蓋的概念。

目錄大綱

  1. Effective Planning of Deep Learning-Driven Projects
  2. Data Preparation for Deep Learning Projects
  3. Developing a Powerful Deep Learning Model
  4. Experiment Tracking, Model Management, and Dataset Versioning
  5. Data Preparation in the Cloud
  6. Efficient Model Training
  7. Revealing the Secret of Deep Learning Models
  8. Simplifying Deep Learning Model Deployment
  9. Scaling a Deep Learning Pipeline
  10. Improving Inference Efficiency
  11. Deep Learning on Mobile Devices
  12. Monitoring Deep Learning Endpoints in Production
  13. Reviewing the Completed Deep Learning Project

目錄大綱(中文翻譯)


  1. Effective Planning of Deep Learning-Driven Projects

  2. Data Preparation for Deep Learning Projects

  3. Developing a Powerful Deep Learning Model

  4. Experiment Tracking, Model Management, and Dataset Versioning

  5. Data Preparation in the Cloud

  6. Efficient Model Training

  7. Revealing the Secret of Deep Learning Models

  8. Simplifying Deep Learning Model Deployment

  9. Scaling a Deep Learning Pipeline

  10. Improving Inference Efficiency

  11. Deep Learning on Mobile Devices

  12. Monitoring Deep Learning Endpoints in Production

  13. Reviewing the Completed Deep Learning Project

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