Experiment Design and Management for Entry-Level Computer Vision Engineers
Master the art of designing, executing, and analyzing computer vision experiments to build robust and high-performing models efficiently.
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Foundations of Experiment Design and Data Management
Unit 1: Introduction to CV Experiment Design
Why Experiment in CV?
Setting Clear Objectives
Hypotheses & Metrics
Unit 2: Experiment Types and Data Splitting
A/B Testing for CV Models
Beyond A/B: Multi-Variate
Train, Val, Test Split
Handling Data Imbalance
Unit 3: Data Management Best Practices
Data Annotation Pipelines
Augment Your Data
Data & Concept Drift
Experiment Tracking, Version Control, and Optimization
Unit 1: Reproducibility with Version Control
Why Version Control?
Git for Code & Models
DVC for Data & Artifacts
Unit 2: Tracking Experiments Effectively
Intro to Experiment Tracking
Logging with MLflow
Visualizing Runs
Unit 3: Optimizing Model Performance
Hyperparameter Tuning Basics
Smart Optimization
Unit 4: Analyzing and Concluding Experiments
Interpreting Results
Iterate & Improve