ECE 595ML Lecture 14.1: Logistic Regression - From Linear to Logistic

By Stanley H. Chan

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

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Researchers should cite this work as follows:

  • Stanley H. Chan (2020), "ECE 595ML Lecture 14.1: Logistic Regression - From Linear to Logistic," https://nanohub.org/resources/32593.

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WTHR 200, Purdue University, West Lafayette, IN

ECE 595ML Lecture 14.1: Logistic Regression - From Linear to Logistic
  • Lecture 14.1: Logistic Regression 1 - From Linear to Logistic 1. Lecture 14.1: Logistic Regress… 0
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  • Overview 2. Overview 15.015015015015015
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  • Outline 3. Outline 105.67233900567234
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  • Geometry of Linear Regression 4. Geometry of Linear Regression 224.92492492492494
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  • From Linear to Logistic Regression 5. From Linear to Logistic Regres… 326.59325992659325
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  • Sigmoid Function 6. Sigmoid Function 478.14481147814485
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  • Sigmoid Function 7. Sigmoid Function 730.26359693026359
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  • Sigmoid Function 8. Sigmoid Function 782.982982982983
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  • Sigmoid Function 9. Sigmoid Function 948.24824824824827
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  • From Linear to Logistic Regression 10. From Linear to Logistic Regres… 1171.5382048715383
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  • Loss Function for Linear Regression 11. Loss Function for Linear Regre… 1183.2832832832833
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  • Training Loss for Logistic Regression 12. Training Loss for Logistic Reg… 1209.2425759092425
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  • Why Not L2 Loss? 13. Why Not L2 Loss? 1566.3997330664
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  • Why Not L2 Loss? 14. Why Not L2 Loss? 1619.9532866199534
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