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- Introduction
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Classroom Contents
ML Projects - Full Stack Deep Learning - Spring 2021
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- 1 - Introduction
- 2 - Why Do ML Projects Fail?
- 3 - Lecture Overview and Running Case Study
- 4 - Lifecycle Thinking about the activities in an ML project
- 5 - Prioritizing Projects Assessing the feasibility and impact of the projects
- 6 - Archetypes Knowing the main categories of projects and implications for project management
- 7 - Metrics Picking a single number to optimize
- 8 - Baselines Figuring out if your model is performing well
- 9 - Conclusion
