Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks
Palczewski, Tomasz, Lee, Jaejun, Mookiah, Lenin
- 出版商: Packt Publishing
- 出版日期: 2022-08-30
- 售價: $2,050
- 貴賓價: 9.5 折 $1,948
- 語言: 英文
- 頁數: 322
- 裝訂: Quality Paper - also called trade paper
- ISBN: 180324366X
- ISBN-13: 9781803243665
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相關分類:
DeepLearning、TensorFlow
下單後立即進貨 (約3~4週)
相關主題
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)
加強您開發強大深度學習模型並有效地使用雲服務進行規模化分佈的技能
主要特點:
- 瞭解如何使用各種可用工具有效執行深度學習項目
- 學習如何使用Amazon Web Services規模化開發PyTorch和TensorFlow模型
- 探索解決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詳細解釋如何將各種模型轉換為部署並全面了解替代方案,來彌合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為另一個框架所需的知識,並學習如何根據部署環境的特定要求對其進行調整。本書還提供了具體的實現和相關方法,幫助您立即應用所學知識。您將親身體驗常用的深度學習框架和用於規模化數據分析的熱門雲服務。此外,您將深入了解作者在大規模部署數百個基於人工智能的服務方面的集體知識。
通過閱讀本書,您將瞭解如何將為概念驗證而開發的模型轉換為針對特定生產環境進行優化的生產就緒應用程序。
學到什麼:
- 瞭解如何使用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應用程序
- 發現如何使用AWS高效建立深度學習流程
- 探索不同的模型壓縮方法以滿足各種部署需求
- 開發在移動設備上運行深度學習的Android和iOS應用程序
- 監控生產環境中的深度學習模型系統
- 選擇適合的系統架構來開發和部署模型
適合對象:
機器學習工程師、深度學習專家和數據科學家將會在本書中找到詳細示例,幫助他們彌合理論與應用之間的差距。具備初級水平的機器學習或軟件工程知識將有助於您輕鬆理解本書中涵蓋的概念。
目錄大綱
- 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
目錄大綱(中文翻譯)
- 深度學習專案的有效規劃
- 深度學習專案的數據準備
- 開發強大的深度學習模型
- 實驗追蹤、模型管理和數據集版本控制
- 雲端中的數據準備
- 高效的模型訓練
- 揭示深度學習模型的秘密
- 簡化深度學習模型部署
- 擴展深度學習流程
- 提升推論效率
- 移動設備上的深度學習
- 監控生產環境中的深度學習端點
- 審查已完成的深度學習專案