Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images (Paperback)
暫譯: 實用機器學習於電腦視覺：圖像的端到端機器學習（平裝本）

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)

這本實用的書籍展示了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，並使用經過驗證的機器學習技術。本書提供了端到端深度學習的良好入門：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 向您展示如何開發準確且可解釋的計算機視覺機器學習模型，並使用穩健的機器學習架構以靈活且可維護的方式將其投入大規模生產。您將學習如何設計、訓練、評估和使用用 TensorFlow 或 Keras 編寫的模型進行預測。

您將學習如何：

- 設計適用於計算機視覺任務的機器學習架構
- 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管道以訓練、評估、部署和解釋您的模型
- 預處理圖像以進行數據增強並支持可學習性
- 融入可解釋性和負責任的人工智慧最佳實踐
- 將圖像模型部署為網路服務或在邊緣設備上運行
- 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 的分析與人工智慧解決方案總監，他領導一個團隊，致力於為各行各業的商業問題建立解決方案。他的使命是讓機器學習民主化，使任何人都能在任何地方進行機器學習。

Martin Görner 是 Keras/TensorFlow 的產品經理，專注於改善開發者在使用最先進模型時的體驗。他對科學、技術、編程、演算法以及所有相關領域充滿熱情。

Ryan Gillard 是 Google Cloud 專業服務組織的人工智慧工程師，他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行業的研究科學家。擁有神經科學和物理學的學位，他喜歡在這些學科的交集處工作，通過數學探索智慧。

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