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https://youtube.com/playlist?list=PLkDaE6sCZn6E7jZ9sN_xHwSHOdjUxUW_b
https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning
Structuring Machine Learning Projects (Course3)
Contents
Week1 ML Strategy (1)
- Improving Model Performance (C3W1L01)
- Orthogonalization (C3W1L02 )
- Single Number Evaluation Metric (C3W1L03)
- Satisficing and Optimizing Metrics (C3W1L04)
- Train/Dev/Test Set Distributions (C3W1L05)
- Sizeof Dev and Test Sets (C3W1L06)
- When to Change Dev/Test Sets (C3W1L07)
- C3W1L08 WhyHumanLevelPerformance
- Avoidable Bias (C3W1L09)
- Understanding Human-Level Performance? (C3W1L10)
- Surpassing Human-Level Performance (C3W1L11)
- Improving Model Performance (C3W1L12)
Week2 ML Strategy (2)
- Carrying Out Error Analysis (C3W2L01)
- Cleaning Up Incorrectly Labelled Data (C3W2L02)
- Build First System Quickly, Then Iterate (C3W2L03)
- Training and Testing on Different Distributions (C3W2L04)
- Bias and Variance With Mismatched Data (C3W2L05)
- Addressing Data Mismatch (C3W2L06)
- Transfer Learning (C3W2L07)
- Multitask Learning (C3W2L08)
- What is end-to-end deep learning? (C3W2L09)
- Whether to Use End-To-End Deep Learning (C3W2L10)
ref.
https://www.deeplearning.ai/thebatch
https://github.com/amanchadha/coursera-deep-learning-specialization
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