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Intro and Logistics (Sara Beery)
[Slides]
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IDEs, Github, and remote workflow (Manuel Knott)
[Video]
[Slides]
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Data Visualization, Splitting and Avoiding Overfitting (Sara Beery)
[Video]
[Slides]
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Staying Organized in Machine Learning Projects (Björn Lütjens)
[Video]
[Slides]
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Working with Open-Source CV codebases - Choosing a baseline model and custom data loading (Sara Beery and Surya Naranyay Hari)
[Video]
[Slides]
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Intro to CV Tasks and Architectures (Suzanne Stathatos)
[Slides]
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Evaluation Metrics (Shir Bar)
[Video]
[Slides]
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Probing your Model’s Performance: Offline Evaluation & Analysis (Sam Lapp)
[Video]
[Slides]
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What's next? Rules of Thumb to Improve Results (Justin Kay)
[Video]
[Slides]
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Squeezing Your Data: Data Augmentation and Self-Supervised Learning (Björn Lütjens & Tarun Sharma)
[Video]
[Slides part 1]
[Slides part 2]
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Experimental Design in Computer Vision (Shir Bar)
[Video]
[Slides]
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Efficient Models and Speed vs Accuracy (Justin Kay)
[Video]
[Slides]
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What Do I Do with my (Imperfect) Model? (Sam Lapp)
[Video]
[Slides]