Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images (Paperback)

Lakshmanan, Valliappa, Görner, Martin, Gillard, Ryan

Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images (Paperback)

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商品描述

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.

Google engineers Valliappa Lakshmanan, Martin Gorner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.

You'll learn how to:

  • Design ML architecture for computer vision tasks
  • Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
  • Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
  • Preprocess images for data augmentation and to support learnability
  • Incorporate explainability and responsible AI best practices
  • Deploy image models as web services or on edge devices
  • Monitor and manage ML models

商品描述(中文翻譯)

這本實用的書籍向您展示如何使用機器學習模型從圖像中提取信息。機器學習工程師和數據科學家將學習如何使用成熟的機器學習技術解決各種圖像問題，包括分類、物體檢測、自編碼器、圖像生成、計數和標題生成。本書提供了一個很好的端到端深度學習入門：數據集創建、數據預處理、模型設計、模型訓練、評估、部署和可解釋性。

Google工程師Valliappa Lakshmanan、Martin Gorner和Ryan Gillard向您展示如何以靈活且可維護的方式開發準確且可解釋的計算機視覺機器學習模型，並將其應用於大規模生產中的堅固機器學習架構。您將學習如何使用TensorFlow或Keras設計、訓練、評估和預測模型。

您將學習如何：
- 為計算機視覺任務設計機器學習架構
- 選擇適合您任務的模型（如ResNet、SqueezeNet或EfficientNet）
- 創建端到端的機器學習流程，以訓練、評估、部署和解釋您的模型
- 預處理圖像以進行數據擴增和支持可學習性
- 納入可解釋性和負責任的人工智能最佳實踐
- 將圖像模型部署為Web服務或邊緣設備上
- 監控和管理機器學習模型

作者簡介

Valliappa (Lak) Lakshmanan is the director of analytics and AI solutions at Google Cloud, where he leads a team building cross-industry solutions to business problems. His mission is to democratize machine learning so that it can be done by anyone anywhere.

Martin Görner is a product manager for Keras/TensorFlow focused on improving the developer experience when using state-of-the-art models. He's passionate about science, technology, coding, algorithms, and everything in between.

Ryan Gillard is an AI engineer in Google Cloud's Professional Services organization, where he builds ML models for a wide variety of industries. He started his career as a research scientist in the hospital and healthcare industry. With degrees in neuroscience and physics, he loves working at the intersection of those disciplines exploring intelligence through mathematics.

作者簡介(中文翻譯)

Valliappa (Lak) Lakshmanan 是 Google Cloud 的分析和人工智慧解決方案總監，領導一個團隊為業務問題建立跨行業解決方案。他的使命是普及機器學習，讓任何人在任何地方都能進行機器學習。

Martin Görner 是 Keras/TensorFlow 的產品經理，專注於改善使用最先進模型時的開發者體驗。他對科學、技術、編碼、算法以及所有相關領域都充滿熱情。

Ryan Gillard 是 Google Cloud 專業服務組織的人工智慧工程師，為各種行業建立機器學習模型。他的職業生涯始於醫院和醫療保健行業的研究科學家。擁有神經科學和物理學學位的他喜歡在這些學科的交叉領域中通過數學探索智能。

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