iT邦幫忙

第 11 屆 iT 邦幫忙鐵人賽

DAY 28
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Introduction

Train an ML model with examples (Training)

  • label - true answer
  • model - mathematical function

Key things to measure how inclusive a machine learning system is

  • Equality of Opportunity
  • Simulating Decisions
  • Finding Errors in your dataset using Facets

Launching into ML

Defining ML Models

  • Parameter - real-valued variable that changes during model training
  • Hyper-parameter - setting that we set before training and doesn't change afterwards

上一篇
Day 27 Art and Science of Machine Learning (cont.)
下一篇
Day 29 Summary (cont.)
系列文
ML Study Jam Journey30

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