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构建机器学习应用()(英文版)【正版新书】

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作者(法)伊曼纽尔·阿米森

出版社东南大学出版社

ISBN9787564189518

出版时间2020-08

装帧平装

开本16开

定价89元

货号1202110578

上书时间2024-06-29

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品相描述：全新

商品描述: 作者简介
    伊曼纽尔·阿米森是Stripe公司的机器学习工程师，曾经为Local Motion和Zipcar公司实施并部署了预测分析和机器学习解决方案。很近，他正在领导洞见数据科学（Insight Data Scierice）的人工智能项目群，指导着100多个机器学习项目。Emmanuel拥有法国三所很好大学的人工智能、计算机工程和管理硕士学位。

目录
Preface
Part I. Find the Correct ML Approach
  1. From Product Goal to ML Framing
    Estimate What Is Possible
      Models
      Data
    Framing the ML Editor
      Trying to Do It All with ML: An End-to-End Framework
      The Simplest Approach: Being the Algorithm
      Middle Ground: Learning from Our Experience
    Monica Rogati: How to Choose and Prioritize ML Projects
    Conclusion
  2. Createa Plan
    Measuring Success
      Business Performance
      Model Performance
      Freshness and Distribution Shift
      Speed
    Estimate Scope and Challenges
      Leverage Domain Expertise
      Stand on the Shoulders of Giants
    ML Editor Planning
      Initial Plan for an Editor
      Always Start with a Simple Model
    To Make Regular Progress: Start Simple
      Start with a Simple Pipeline
      Pipeline for the ML Editor
    Conclusion
Part II. Build a Working Pipeline
  3. Build Your First End-to-End Pipeline
    The Simplest Scaffolding
    Prototype of an ML Editor
      Parse and Clean Data
      Tokenizing Text
      Generating Features
    Test Your Workflow
      User Experience
      Modeling Results
    ML Editor Prototype Evaluation
      Model
      User Experience
    Conclusion
  4. Acquire an Initial Dataset
    Iterate on Datasets
      Do Data Science
    Explore Your First Dataset
      Be Efficient, Start Small
      Insights Versus Products
      A Data Quality Rubric
    Label to Find Data Trends
      Summary Statistics
      Explore and Label Efficiently
      Be the Algorithm
      Data Trends
    Let Data Inform Features and Models
      Build Features Out of Patterns
      ML Editor Features
    Robert Munro: How Do You Find, Label, and Leverage Data?
    Conclusion
Part III. Iterate on Models
  5. Train and Evaluate Your Model
    The Simplest Appropriate Model
      Simple Models
      From Patterns to Models
      Split Your Dataset
      ML Editor Data Split
      Judge Performance
    Evaluate Your Model: Look Beyond Accuracy
      Contrast Data and Predictions
      Confusion Matrix
      ROC Curve
      Calibration Curve
      Dimensionality Reduction for Errors
      The Top-k Method
      Other Models
    Evaluate Feature Importancek
      Directly from a Classifier
      Black-Box Explainers
    Conclusion
  6. Debug Your ML Problems
    Software Best Practices
      ML-Specific Best Practices
    Debug Wiring: Visualizing and Testing
      Start with One Example
      Test Your ML Code
    Debug Training: Make Your Model Learn
      Task Difficulty
      Optimization Problems
    Debug Generalization: Make Your Model Useful
      Data Leakage
      Overfitting
      Consider the Task at Hand
    Conclusion
  7. Using Classifiers for Writing Recommendations
    Extracting Recommendations from Models
      What Can We Achieve Without a Model?
      Extracting Global Feature Importance
      Using a Models Score
      Extracting Local Feature Importance
    Comparing Models
      Version 1: The Report Card
      Version 2: More Powerful, More Unclear
      Version 3: Understandable Recommendations
    Generating Editing Recommendations
    Conclusion
Part IV. Deploy and Monitor
  8. Considerations When Deploying Models
    Data Concerns
      Data Ownership
      Data Bias
      Systemic Bias
    Modeling Concerns
      Feedback Loops
      Inclusive Model Performance
      Considering Context
      Adversaries
      Abuse Concerns and Dual-Use
    Chris Harland: Shipping Experiments
     Conclusion
  9. Choose Your Deployment Option
    Server-Side Deployment
      Streaming Application or API
      Batch Predictions
    Client-Side Deployment
      On Device
      Browser Side
    Federated Learning: A Hybrid Approach
    Conclusion
  10. Build Safeguards for Models
    Engineer Around Failures
      Input and Output Checks
      Model Failure Fal

内容摘要
    学习设计、构建和部署机器学习（ML）应用所需的技能。通过这本实用的教程，你将构建一个机器学习驱动的示例应用程序，将很初的想法转化成可部署的产品。数据科学家、软件工程师和产品经理一一无论经验丰富的的专家还是刚刚入门的新手一一都可以循序渐进地学习构建实际的机器学习应用程序所涉及的工具、很好实践和技术挑战。
    作者Emmanuel Ameisen是一名经验丰富的数据科学家，他领导着一个人工智能教育项目群，通过代码片段、插图和屏幕截图以及对行业的采访内容展示实用的机器学习概念。本书部分教授如何设计一个机器学习应用程序并评估效果；第二部分介绍如何构建一个可用的机器学习模型；第三部分演示改进模型的方法，让模型满足你很初的设想；第四部分介绍应用部署和监测策略。
    这本书将帮助你：
    定义产品目标，确立一个机器学习问题；
    快速构建一个端到端机器学习流水线并获取一个初始数据集；
    培训和评估机器学习模型并解决性能瓶颈；
    在生产环境中部署和监测模型。

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