Tensorflow 2 Pocket Reference: Building and Deploying Machine Learning Models (TensorFlow 2 口袋參考：構建與部署機器學習模型)

Tung, Kc

Tensorflow 2 Pocket Reference: Building and Deploying Machine Learning Models
  • 出版商: O'Reilly
  • 出版日期: 2021-08-24
  • 定價: $1,050
  • 售價: 9.5 折 $998
  • 貴賓價: 9.0 折 $945
  • 語言: 英文
  • 頁數: 256
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1492089184
  • ISBN-13: 9781492089186
  • 相關分類: DeepLearning、TensorFlow、Machine Learning

    • 立即出貨 (庫存=1)

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商品描述

This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.

When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.

  • Understand best practices in TensorFlow model patterns and ML workflows
  • Use code snippets as templates in building TensorFlow models and workflows
  • Save development time by integrating prebuilt models in TensorFlow Hub
  • Make informed design choices about data ingestion, training paradigms, model saving, and inferencing
  • Address common scenarios such as model design style, data ingestion workflow, model training, and tuning

商品描述(中文翻譯)

這本以Python為基礎的TensorFlow 2設計模式易於使用，將幫助您在各種使用情境下做出明智的決策。作者KC Tung討論了企業數據科學和機器學習實踐中的常見主題和任務，而不是專注於TensorFlow本身。

您何時以NumPy或流式數據集的形式提供訓練數據？如何在訓練過程中設置交叉驗證？如何利用預訓練模型進行轉移學習？如何進行超參數調整？閱讀這本便攜參考書，減少您在TensorFlow使用情境中尋找選項所花費的時間。

本書內容包括：
- 理解TensorFlow模型模式和機器學習工作流程的最佳實踐
- 使用程式碼片段作為構建TensorFlow模型和工作流程的模板
- 通過集成TensorFlow Hub中的預建模型節省開發時間
- 對於數據輸入、訓練範式、模型保存和推論等方面做出明智的設計選擇
- 解決常見情境，如模型設計風格、數據輸入工作流程、模型訓練和調整等。

作者簡介

KC Tung is a cloud solution architect in Microsoft who specializes in designing and delivering machine learning and AI solutions in enterprise cloud architecture. He helps enterprise customers with use-case driven architecture, AI/ML model development/deployment in the cloud, and technology selection and integration best suited for their requirements. He is a Microsoft certified AI engineer and data engineer. He has a PhD in molecular biophysics from the University of Texas Southwestern Medical, and has spoken at the 2018 O'Reilly AI Conference in San Francisco and the 2019 O'Reilly Tensorflow World Conference in San Jose.

作者簡介(中文翻譯)

KC Tung 是一位在微軟擔任雲端解決方案架構師的專家，專注於在企業雲架構中設計和提供機器學習和人工智慧解決方案。他協助企業客戶進行以用例為驅動的架構設計，以及在雲端中進行人工智慧/機器學習模型的開發/部署，並選擇和整合最適合其需求的技術。他是微軟認證的人工智慧工程師和資料工程師。他擁有德州大學西南醫學中心的分子生物物理學博士學位，並曾在2018年奧萊利人工智慧大會（O'Reilly AI Conference）和2019年奧萊利Tensorflow世界大會（O'Reilly Tensorflow World Conference）上發表演講。

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