The Deep Learning Architect's Handbook: Build and deploy production-ready DL solutions leveraging the latest Python techniques

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle

Key Features:

• Interpret your models' decision-making process, ensuring transparency and trust in your AI-powered solutions
• Gain hands-on experience in every step of the deep learning life cycle
• Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.

This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You'll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency.

As you progress, you'll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You'll also discover the transformative potential of large language models (LLMs) for a wide array of applications.

By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.

What You Will Learn:

• Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs)
• Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model
• Deal with multi-modal data drift in a production environment
• Evaluate the quality and bias of your models
• Explore techniques to protect your model from adversarial attacks
• Get to grips with deploying a model with DataRobot AutoML

Who this book is for:

This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

發揮深度學習的力量，以實用指南掌握整個深度學習生命週期的技巧和最佳實踐，提高生產力和效率。

主要特點：
- 解釋模型的決策過程，確保AI解決方案的透明度和信任度。
- 在深度學習生命週期的每個步驟中獲得實踐經驗。
- 探索案例研究和部署DL模型的解決方案，同時解決可擴展性、數據漂移和道德考慮等問題。
- 購買印刷版或Kindle電子書，可獲得免費PDF電子書。

書籍描述：
深度學習使自動化實現了以前無法達到的成就，但如何從中獲得真實的商業價值是一項艱巨的任務。本書將教您如何構建複雜的深度學習模型，並通過結構化數據來實現深度學習目標。

本書探討了深度學習生命週期的每個方面，從計劃和數據準備到模型部署和治理，使用真實世界的場景，引導您創建、部署和管理先進的解決方案。您還將學習如何使用深度學習架構處理圖像、音頻、文本和視頻數據，以及如何客觀地優化和評估深度學習模型，解決偏見、公平性、對抗性攻擊和模型透明度等問題。

隨著學習的進展，您將利用AI平台來簡化深度學習生命週期，並利用Python庫和框架（如PyTorch、ONNX、Catalyst、MLFlow、Captum、Nvidia Triton、Prometheus和Grafana）來執行高效的深度學習架構，優化模型性能並簡化部署流程。您還將發現大型語言模型（LLMs）在各種應用中的轉型潛力。

通過閱讀本書，您將掌握深度學習技術，充分發揮其在各種領域的潛力。

您將學到什麼：
- 使用神經架構搜索（NAS）自動設計人工神經網絡（ANNs）。
- 實現循環神經網絡（RNNs）、卷積神經網絡（CNNs）、BERT、transformers等模型。
- 在生產環境中處理多模態數據漂移。
- 評估模型的質量和偏見。
- 探索保護模型免受對抗性攻擊的技術。
- 掌握使用DataRobot AutoML部署模型的方法。

本書適合對深度學習架構解決複雜商業問題感興趣的深度學習從業人員、數據科學家和機器學習開發人員。廣泛的深度學習和人工智能領域的專業人士也將從中受益，適用於各種商業用例。開始閱讀本書需要具備Python編程的工作知識和基本的深度學習技術理解能力。