Tag Archives: Coursera

Deep Learning Note 3 — Comparing to Human Level Performance

Following up last post Deep Learning Note 3 — Machine Learning Strategy of Setting up the Learning Target on the Machine Learning Strategy, this post covers the last 3 points on how to work on a machine learning project and accelerate the project iteration. Topics covered in this post are marked in bold in the Learning Objectives. This post is also published on Steemit.

Learning Objectives

  • Understand why Machine Learning strategy is important
  • Apply satisfying and optimizing metrics to set up your goal for ML projects
  • Choose a correct train/dev/test split of your dataset
  • Understand how to define human-level performance
  • Use human-level performance to define your key priorities in ML projects
  • Take the correct ML Strategic decision based on observations of performances and dataset

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Deep Learning Note 3 — Machine Learning Strategy of Setting up the Learning Target

This week’s course Machine Learning Strategy mainly focuses on how to work on a machine learning project and accelerate the project iteration. Since this course covers quite a lot small topics, I’ll break down my notes to several shorter posts. Topics covered in this post are marked in bold in the Learning Objectives. This post is also published on Steemit.

Previous Notes:

Learning Objectives

  • Understand why Machine Learning strategy is important
  • Apply satisfying and optimizing metrics to set up your goal for ML projects
  • Choose a correct train/dev/test split of your dataset
  • Understand how to define human-level performance
  • Use human-level performance to define your key priorities in ML projects
  • Take the correct ML Strategic decision based on observations of performances and dataset

Continue reading