AL-503 (B) · Deep Learning/Official Syllabus
Deep Learning (AL-503 (B)) - Official Syllabus (AL-503)
1
Unit 1
UNIT-1 :
- Introduction What is optimization
- Formulation of LPP Solution of LPP: Simplex method
- Basic Calculus for optimization: Limits and multivariate functions
- Derivatives and linear approximations: Single variate functions and multivariate functions.
2
Unit 2
UNIT- 2 :
- Machine Learning Strategy ML readiness
- Risk mitigation
- Experimental mindset Build/buy/partner setting up a team
- Understanding and communicating change
3
Unit 3
UNIT-3 :
- Responsible Machine Learning AI for good and all
- Positive feedback loops and negative feedback loops
- Metric design and observing behaviours
- Secondary effects of optimization
- Regulatory concerns.
4
Unit 4
UNIT-4 :
- Machine Learning in production and planning Integrating info systems
- users break things time and space complexity in production
- when to retain the model?
- Logging ML model versioning
- Knowledge transfer
- Reporting performance to stakeholders.
5
Unit 5
UNIT-5 :
- Care and feeding of your machine learning model MLPL Recap
- Post deployment challenges
- QUAM monitoring and logging QUAM Testing QUAM maintenance QUAM updating
- Separating Data stack from Production
- Dashboard Essentials and Metrics monitoring.
6
Unit 6
== END OF UNITS==
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