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CourseClub NET Udacity Computer Vision Nanodegree v1

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[CourseClub.NET] Udacity - Computer Vision Nanodegree v1.0.0

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2.9 GB

Total Files

2434

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ABC1752DE3927AFA6F6C3C9CE10CAB56DF90185B

/Part 01-Module 01-Lesson 01_Welcome to Computer Vision/

01. 1 HS Welcome To Computer Vision V2Demo1 V2-9s-gm2ZvODI.en.vtt

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01. 1 HS Welcome To Computer Vision V2Demo1 V2-9s-gm2ZvODI.mp4

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01. 1 HS Welcome To Computer Vision V2Demo1 V2-9s-gm2ZvODI.zh-CN.vtt

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01. Welcome to the Computer Vision Nanodegree Program.html

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02. 2 HSA Computer Vision In Industry V3 Demo02 V3-_8be3GdqfqU.en.vtt

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02. 2 HSA Computer Vision In Industry V3 Demo02 V3-_8be3GdqfqU.mp4

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02. 2 HSA Computer Vision In Industry V3 Demo02 V3-_8be3GdqfqU.zh-CN.vtt

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02. Computer Vision in Industry.html

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03. 3 HSA Course Outline V3 Demo01 1 V3-INMNyJGB2DI.en.vtt

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03. 3 HSA Course Outline V3 Demo01 1 V3-INMNyJGB2DI.mp4

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03. 3 HSA Course Outline V3 Demo01 1 V3-INMNyJGB2DI.zh-CN.vtt

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03. Course Outline.html

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04. Projects and Topics.html

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05. Meet Danny Shapiro At Nvidia-wm1aZZvF6Is.en.vtt

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05. Meet Danny Shapiro At Nvidia-wm1aZZvF6Is.mp4

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05. Meet Danny Shapiro At Nvidia-wm1aZZvF6Is.zh-CN.vtt

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05. Partnership with Industry.html

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06. 4 HSA Learning In The Classroom V3 Demov1 V3-KN_SROb6SnM.en.vtt

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06. 4 HSA Learning In The Classroom V3 Demov1 V3-KN_SROb6SnM.mp4

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06. 4 HSA Learning In The Classroom V3 Demov1 V3-KN_SROb6SnM.zh-CN.vtt

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06. Learning in the Classroom.html

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07. Deadline Policy.html

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08. Udacity Support.html

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09. Community Guidelines.html

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10. Moving Forward V4-E35QVmp1f2k.en.vtt

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10. Moving Forward V4-E35QVmp1f2k.mp4

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10. Moving Forward V4-E35QVmp1f2k.zh-CN.vtt

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10. Moving Forward!.html

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index.html

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/img/

screen-shot-2018-03-23-at-4.50.34-pm.png

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screen-shot-2018-05-25-at-10.42.15-am.png

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study-group.png

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/Part 01-Module 01-Lesson 02_Image Representation Classification/

01. 01 Pattern Recognition V1-cX5EE1WEqhY.en.vtt

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01. 01 Pattern Recognition V1-cX5EE1WEqhY.mp4

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01. 01 Pattern Recognition V1-cX5EE1WEqhY.zh-CN.vtt

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01. Intro to Pattern Recognition.html

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02. 05. Emotional Intelligence-D_LzJsJH5qk.en.vtt

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02. 05. Emotional Intelligence-D_LzJsJH5qk.mp4

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02. 05. Emotional Intelligence-D_LzJsJH5qk.zh-CN.vtt

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02. Emotional Intelligence.html

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03. 08. Computer Vision Pipeline-64hFcqhnNow.en.vtt

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03. 08. Computer Vision Pipeline-64hFcqhnNow.mp4

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03. 08. Computer Vision Pipeline-64hFcqhnNow.zh-CN.vtt

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03. Computer Vision Pipeline.html

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04. 09. Training a Model-m4GVfwVkj74.en.vtt

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04. 09. Training a Model-m4GVfwVkj74.mp4

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04. 09. Training a Model-m4GVfwVkj74.zh-CN.vtt

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04. Training a Model.html

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05. Separating Data.html

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06. AffdexMe Demo-dpFtXDqakvY.en.vtt

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06. AffdexMe Demo-dpFtXDqakvY.mp4

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06. AffdexMe Demo-dpFtXDqakvY.zh-CN.vtt

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06. AffdexMe Demo.html

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07. Image Formation-6ZVnQYzfpis.en.vtt

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07. Image Formation-6ZVnQYzfpis.mp4

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07. Image Formation-6ZVnQYzfpis.zh-CN.vtt

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07. Image Formation.html

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08. Images as Grids of Pixels-RVNiaZuv6Ss.en.vtt

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08. Images as Grids of Pixels-RVNiaZuv6Ss.mp4

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08. Images as Grids of Pixels-RVNiaZuv6Ss.zh-CN.vtt

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08. Images as Grids of Pixels.html

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09. Notebook Images as Numerical Data.html

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10. Color Images--XbXiiGQ9gw.en.vtt

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10. Color Images--XbXiiGQ9gw.mp4

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10. Color Images--XbXiiGQ9gw.zh-CN.vtt

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10. Color Images.html

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11. Color or Grayscale.html

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12. Notebook Visualizing RGB Channels.html

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13. Color Thresholds-08ZlYZJaiUg.en.vtt

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13. Color Thresholds-08ZlYZJaiUg.mp4

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13. Color Thresholds-08ZlYZJaiUg.zh-CN.vtt

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13. Color Thresholds.html

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14. Coding A Blue Screen-jeeDryFxodk.en.vtt

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14. Coding A Blue Screen-jeeDryFxodk.mp4

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14. Coding A Blue Screen-jeeDryFxodk.zh-CN.vtt

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14. Coding a Blue Screen.html

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15. Notebook Blue Screen.html

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16. Notebook Green Screen.html

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17. Color Spaces and Transforms-B350aJVSsFc.en.vtt

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17. Color Spaces and Transforms-B350aJVSsFc.mp4

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17. Color Spaces and Transforms-B350aJVSsFc.zh-CN.vtt

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17. Color Spaces and Transforms.html

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18. Notebook Color Conversion.html

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19. Day and Night Classification Challenge.html

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20. Notebook Load and Visualize the Data.html

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21. Labeled Data and Accuracy-FN96OM_JGyM.en.vtt

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21. Labeled Data and Accuracy-FN96OM_JGyM.mp4

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21. Labeled Data and Accuracy-FN96OM_JGyM.zh-CN.vtt

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21. Labeled Data and Accuracy.html

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22. Distinguishing Traits.html

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23. Features.html

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23. Nd113 C7 29 L Features-HshygbfQylA.en.vtt

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23. Nd113 C7 29 L Features-HshygbfQylA.mp4

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23. Nd113 C7 29 L Features-HshygbfQylA.zh-CN.vtt

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24. Standardizing Output.html

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25. Notebook Standardizing Day and Night Images.html

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26. Average Brightness.html

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26. Nd113 C7 32 L Average Brightness V2-oUlOS670uQg.en.vtt

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26. Nd113 C7 32 L Average Brightness V2-oUlOS670uQg.mp4

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26. Nd113 C7 32 L Average Brightness V2-oUlOS670uQg.zh-CN.vtt

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27. Notebook Average Brightness Feature Extraction.html

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28. Classification.html

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28. Nd113 C7 45 L Classification V1-LWD1M2vqXXo.en.vtt

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28. Nd113 C7 45 L Classification V1-LWD1M2vqXXo.mp4

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28. Nd113 C7 45 L Classification V1-LWD1M2vqXXo.zh-CN.vtt

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29. Notebook Classification.html

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30. Evaluation Metrics.html

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30. Nd113 C7 46 L Evaluation Metrics-fDN4D1QV674.en.vtt

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30. Nd113 C7 46 L Evaluation Metrics-fDN4D1QV674.mp4

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30. Nd113 C7 46 L Evaluation Metrics-fDN4D1QV674.zh-CN.vtt

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31. Notebook Accuracy and Misclassification.html

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32. Review and the Computer Vision Pipeline.html

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index.html

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/img/

screen-shot-2017-03-13-at-12.36.54-pm.png

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stop-sign-classification.png

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/Part 01-Module 01-Lesson 03_Convolutional Filters and Edge Detection/

01. Filters and Finding Edges.html

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01. Nd113 C7 36 L Filters And Finding Edges V1-f3H2EtiZLOQ.en.vtt

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01. Nd113 C7 36 L Filters And Finding Edges V1-f3H2EtiZLOQ.mp4

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01. Nd113 C7 36 L Filters And Finding Edges V1-f3H2EtiZLOQ.zh-CN.vtt

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02. Frequency in Images.html

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03. Notebook Fourier Transforms.html

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04. Quiz Fourier Tranform Image.html

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05. High-pass Filters-OpcFn_H2V-Q.en.vtt

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05. High-pass Filters-OpcFn_H2V-Q.mp4

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05. High-pass Filters-OpcFn_H2V-Q.pt-BR.vtt

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05. High-pass Filters-OpcFn_H2V-Q.zh-CN.vtt

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05. High-pass Filters.html

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06. Quiz Kernels.html

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07. Creating a Filter-VEsdTRBH3D8.en.vtt

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07. Creating a Filter-VEsdTRBH3D8.mp4

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07. Creating a Filter-VEsdTRBH3D8.zh-CN.vtt

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07. Creating a Filter.html

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08. Gradients and Sobel Filters.html

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09. Notebook Finding Edges.html

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10. Low-pass Filters-jy5Be9vf2rk.en.vtt

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10. Low-pass Filters-jy5Be9vf2rk.mp4

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10. Low-pass Filters-jy5Be9vf2rk.zh-CN.vtt

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10. Low-pass Filters.html

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11. Gaussian Blur-1tlPonqIOrU.en.vtt

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11. Gaussian Blur-1tlPonqIOrU.mp4

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11. Gaussian Blur-1tlPonqIOrU.zh-CN.vtt

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11. Gaussian Blur.html

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12. Notebook Gaussian Blur.html

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13. Notebook Fourier Transforms of Filters.html

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14. Convolutional Layer.html

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15. Canny Edge Detection-Nm1H2xMMS3I.en.vtt

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15. Canny Edge Detection-Nm1H2xMMS3I.mp4

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15. Canny Edge Detection-Nm1H2xMMS3I.zh-CN.vtt

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15. Canny Edge Detector.html

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16. Notebook Canny Edge Detection.html

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17. Shape Detection.html

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18. Hough Transform-RIOzsF1_xuo.en.vtt

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18. Hough Transform-RIOzsF1_xuo.mp4

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18. Hough Transform-RIOzsF1_xuo.zh-CN.vtt

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18. Hough Transform.html

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19. Quiz Hough Space.html

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20. Hough Line Detection-PVIUkQKbrUw.en.vtt

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20. Hough Line Detection-PVIUkQKbrUw.mp4

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20. Hough Line Detection-PVIUkQKbrUw.zh-CN.vtt

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20. Hough Line Detection.html

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21. Notebook Hough Detections.html

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22. Object Recognition Introducing Haar Cascades.html

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23. Haar Cascades-vALIpxVfKRc.en.vtt

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23. Haar Cascades-vALIpxVfKRc.mp4

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23. Haar Cascades-vALIpxVfKRc.zh-CN.vtt

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23. Haar Cascades.html

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24. Notebook Haar Cascade Face Detection.html

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25. Face Recognition and the Dangers of Bias.html

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26. Beyond Edges, Selecting Different Features.html

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index.html

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/img/

diag-stripes.png

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haar-2-gif.gif

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screen-shot-2017-06-23-at-5.41.04-pm.png

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/Part 01-Module 01-Lesson 04_Types of Features Image Segmentation/

01. Types Of Features-cJHro29nzgg.en.vtt

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01. Types Of Features-cJHro29nzgg.mp4

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01. Types Of Features-cJHro29nzgg.zh-CN.vtt

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01. Types of Features.html

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02. Corner Detectors-jemzDq07MEI.en.vtt

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02. Corner Detectors-jemzDq07MEI.mp4

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02. Corner Detectors-jemzDq07MEI.zh-CN.vtt

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02. Corner Detectors.html

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03. Notebook Find the Corners.html

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04. Dilation and Erosion.html

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05. Image Segmentation.html

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06. Image Contours-Wcbrl7Wr_kU.en.vtt

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06. Image Contours-Wcbrl7Wr_kU.mp4

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06. Image Contours-Wcbrl7Wr_kU.zh-CN.vtt

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06. Image Contours.html

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07. Notebook Find Contours and Features.html

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08. Solution Find Contours and Features.html

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09. K-means Clustering-Cf_LSDCEBzk.en.vtt

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09. K-means Clustering-Cf_LSDCEBzk.mp4

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09. K-means Clustering-Cf_LSDCEBzk.zh-CN.vtt

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09. K-means Clustering.html

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10. K-means Implementation-poKlg-aB4rU.en.vtt

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10. K-means Implementation-poKlg-aB4rU.mp4

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10. K-means Implementation-poKlg-aB4rU.zh-CN.vtt

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10. K-means Implementation.html

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11. Notebook K-means Clustering.html

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index.html

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/img/

screen-shot-2017-06-30-at-3.22.40-pm.png

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/Part 01-Module 01-Lesson 05_Feature Vectors/

01. Corners and Object Detection.html

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02. Feature Vectors--PF1_MITrOw.en.vtt

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02. Feature Vectors--PF1_MITrOw.mp4

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02. Feature Vectors--PF1_MITrOw.zh-CN.vtt

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02. Feature Vectors.html

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03. 01 Realtime Feature Detection V2-zPxylrXf-Gs.en.vtt

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03. 01 Realtime Feature Detection V2-zPxylrXf-Gs.mp4

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03. 01 Realtime Feature Detection V2-zPxylrXf-Gs.zh-CN.vtt

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03. Real-Time Feature Detection.html

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04. 02 Introduction To ORB V3-WN37zcMhMas.en.vtt

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04. 02 Introduction To ORB V3-WN37zcMhMas.mp4

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04. 02 Introduction To ORB V3-WN37zcMhMas.zh-CN.vtt

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04. Introduction to ORB.html

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05. 03 Fast V3-DCHAc6fjcVM.en.vtt

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05. 03 Fast V3-DCHAc6fjcVM.mp4

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05. 03 Fast V3-DCHAc6fjcVM.zh-CN.vtt

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05. FAST.html

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06. Quiz FAST Keypoints.html

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07. 04 Brief V4-EKIPEPpRciw.en.vtt

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07. 04 Brief V4-EKIPEPpRciw.mp4

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07. 04 Brief V4-EKIPEPpRciw.zh-CN.vtt

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07. BRIEF.html

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08. 05 Scale And RotationInvariance V2-2k3T6rfjvx0.en.vtt

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08. 05 Scale And RotationInvariance V2-2k3T6rfjvx0.mp4

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08. 05 Scale And RotationInvariance V2-2k3T6rfjvx0.zh-CN.vtt

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08. Scale and Rotation Invariance.html

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09. Notebook Image Pyramids.html

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10. 07 Feature Matching V2-RH05Wnl1-2A.en.vtt

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10. 07 Feature Matching V2-RH05Wnl1-2A.mp4

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10. 07 Feature Matching V2-RH05Wnl1-2A.zh-CN.vtt

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10. Feature Matching.html

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11. 08 ORB In Video V2 (1)-Vzs6B1dFQC0.en.vtt

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11. 08 ORB In Video V2 (1)-Vzs6B1dFQC0.mp4

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11. 08 ORB In Video V2 (1)-Vzs6B1dFQC0.zh-CN.vtt

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11. ORB in Video.html

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12. Notebook Implementing ORB.html

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13. HOG.html

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13. Histogram of Oriented Gradients-dqe9zGtxoNM.en.vtt

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13. Histogram of Oriented Gradients-dqe9zGtxoNM.mp4

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13. Histogram of Oriented Gradients-dqe9zGtxoNM.zh-CN.vtt

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14. Notebook Implementing HOG.html

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15. Learning to Find Features.html

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index.html

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/img/

screen-shot-2018-04-19-at-4.47.30-pm.png

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screen-shot-2018-04-23-at-5.52.20-pm.png

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/Part 01-Module 01-Lesson 06_CNN Layers and Feature Visualization/

01. 02 Intro To CNN Layers V1 RENDER V3-hT6zBYCuAfw.en.vtt

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01. 02 Intro To CNN Layers V1 RENDER V3-hT6zBYCuAfw.mp4

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01. 02 Intro To CNN Layers V1 RENDER V3-hT6zBYCuAfw.zh-CN.vtt

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01. Introduction to CNN Layers.html

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02. Review Training a Neural Network.html

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03. 03 Data And Lesson Outline RENDER V2-jPr-5aZA6NE.en.vtt

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03. 03 Data And Lesson Outline RENDER V2-jPr-5aZA6NE.mp4

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03. 03 Data And Lesson Outline RENDER V2-jPr-5aZA6NE.zh-CN.vtt

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03. Lesson Outline and Data.html

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04. CNN Architecture, VGG-16.html

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05. Convolutional Layers (Part 2)-LX-yVob3c28.en.vtt

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05. Convolutional Layers (Part 2)-LX-yVob3c28.mp4

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05. Convolutional Layers (Part 2)-LX-yVob3c28.zh-CN.vtt

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05. Convolutional Layers.html

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06. Defining Layers in PyTorch.html

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07. Notebook Visualizing a Convolutional Layer.html

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08. Pooling, VGG-16 Architecture.html

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09. Pooling Layers-OkkIZNs7Cyc.en.vtt

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09. Pooling Layers-OkkIZNs7Cyc.mp4

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09. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt

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09. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt

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09. Pooling Layers.html

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10. Notebook Visualizing a Pooling Layer.html

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11. Fully-Connected Layers, VGG-16.html

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12. Notebook Visualizing FashionMNIST.html

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13. Training in PyTorch.html

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14. Notebook Fashion MNIST Training Exercise.html

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15. Notebook FashionMNIST, Solution 1.html

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16. Dropout-Ty6K6YiGdBs.en.vtt

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16. Dropout-Ty6K6YiGdBs.mp4

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16. Dropout-Ty6K6YiGdBs.pt-BR.vtt

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16. Dropout-Ty6K6YiGdBs.zh-CN.vtt

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16. Review Dropout.html

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16. 动量-r-rYz_PEWC8.en.vtt

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16. 动量-r-rYz_PEWC8.mp4

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17. Notebook FashionMNIST, Solution 2.html

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18. Network Structure.html

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19. 04 Feature Visualization V1 RENDER V2-xwGa7RFg1EQ.en.vtt

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20. 05 Feature Maps V1RENDER V3-oRhsJHHWtu8.en.vtt

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21. 06 First Convolutional Layer T1 V1 RENDER V2-hIHDMWVSfsM.en.vtt

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21. First Convolutional Layer.html

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22. Visualizing CNNs (Part 2).html

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23. 10 Visualizing Activations V1 RENDER V2-CJLNTOXqt3I.en.vtt

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24. Notebook Feature Viz for FashionMNIST.html

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25. Notebook Visualize Your Net Layers.html

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27. Occlusion, Saliency, and Guided Backpropagation.html

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29. Image Classification Regression Challenges.html

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index.html

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/Part 01-Module 01-Lesson 07_Project Facial Keypoint Detection/

01. Project Overview.html

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02. Workspaces Best Practices.html

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03. Project Facial Keypoint Detection.html

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Project Description - Facial Keypoint Detection.html

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Project Rubric - Facial Keypoint Detection.html

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index.html

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/Part 02-Module 01-Lesson 01_Advanced CNN Architectures/

01. 01 CNNs And Scene Understanding RENDER Full V2-_iRqSOsTBQU.en.vtt

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01. 01 CNNs And Scene Understanding RENDER Full V2-_iRqSOsTBQU.mp4

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01. CNN's and Scene Understanding.html

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02. 02 More Than Classification RENDER V2-vBE5KvvAYzg.en.vtt

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04. 04 Bounding Boxes And Regression V1 RENDER V3-2YM82c7SaCo.en.vtt

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10. 09 Detection Without Proposals Summary V1-IMnt1HFu_nc.en.vtt

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/Part 02-Module 01-Lesson 02_YOLO/

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01. 01 Introduction V3-uyefSrHZesY.mp4

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01. Introduction to YOLO.html

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02. 02 YOLO Output V2-MyOuuwk0qC4.en.vtt

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02. 02 YOLO Output V2-MyOuuwk0qC4.mp4

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02. YOLO Output.html

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03. 03 A Convolutional Approach To Sliding Windows V3-8qYqqibIz90.en.vtt

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03. 03 A Convolutional Approach To Sliding Windows V3-8qYqqibIz90.mp4

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03. Sliding Windows, Revisited.html

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04. CNN Sliding Windows .html

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05. 04 Using A Grid To Improve Localization V2-OmgR35Go79Y.en.vtt

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10. Intersection over Union (IoU).html

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11. Quiz IoU and Overlap Limits.html

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14. 11 YOLO Algorithm V3-ZbQzCHQ8YEo.en.vtt

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14. YOLO Algorithm.html

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15. Notebook YOLO Implementation.html

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/Part 02-Module 01-Lesson 03_RNN's/

01. RNN's in Computer Vision.html

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02. RNN Introduction.html

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.en.vtt

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08. Backpropagation- Theory.html

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.en.vtt

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10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.en.vtt

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10. Backpropagation- Example (part b).html

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11. Backpropagation Quiz.html

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12. RNN (part a).html

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14. RNN- Unfolded Model.html

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.en.vtt

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17. Backpropagation Through Time (part a).html

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19. Backpropagation Through Time (part c).html

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23. Some more math.html

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24. RNN Summary-nXP0oGGRrO8.en.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.en.vtt

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25. From RNN to LSTM.html

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index.html

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/Part 02-Module 01-Lesson 04_ Long Short-Term Memory Networks (LSTMs)/

01. Intro to LSTM.html

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02. RNN Vs LSTM-70MgF-IwAr8.en.vtt

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02. RNN Vs LSTM-70MgF-IwAr8.mp4

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02. RNN vs LSTM.html

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03. Basics of LSTM.html

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03. LSTM Basics-gjb68a4XsqE.en.vtt

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03. LSTM Basics-gjb68a4XsqE.mp4

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04. Architecture of LSTM.html

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04. LSTM Architecture-ycwthhdx8ws.en.vtt

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04. LSTM Architecture-ycwthhdx8ws.mp4

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04. LSTM Architecture-ycwthhdx8ws.pt-BR.vtt

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05. Notebook LSTM Structure and Hidden State, PyTorch.html

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06. Learn Gate-aVHVI7ovbHY.en.vtt

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06. Learn Gate-aVHVI7ovbHY.mp4

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06. The Learn Gate.html

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08. The Remember Gate.html

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09. The Use Gate.html

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10. Putting It All Together-IF8FlKW-Zo0.en.vtt

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10. Putting it All Together.html

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11. Quiz.html

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12. Notebook LSTM for Part of Speech Tagging.html

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13. Character-Level RNN.html

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13. Character-Wise RNN-m_S2hs6-j5w.en.vtt

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14. Sequence Batching-pdSr5F-9qE0.en.vtt

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14. Sequence Batching.html

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15. Notebook Character-Level LSTM.html

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16. Other Architectures-MsxFDuYlTuQ.en.vtt

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16. Other architectures.html

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index.html

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/img/

meme.png

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/Part 02-Module 01-Lesson 05_Hyperparameters/

01. Introducing Jay.html

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02. Introduction-erwnzFD7AeE.en.vtt

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02. Introduction-erwnzFD7AeE.mp4

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02. Introduction.html

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03. Learning Rate-HLMjeDez7ps.en.vtt

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03. Learning Rate-HLMjeDez7ps.mp4

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03. Learning Rate.html

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04. Learning Rate.html

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05. Minibatch Size-GrrO1NFxaW8.en.vtt

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05. Minibatch Size.html

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06. Number Of Iterations-TTdHpSb4DV8.en.vtt

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06. Number of Training Iterations Epochs.html

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08. RNN Hyperparameters-yQvnv7l_aUo.en.vtt

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08. RNN Hyperparameters.html

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10. Sources References.html

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index.html

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/Part 02-Module 01-Lesson 06_Optional Attention Mechanisms/

01. 01 Introduction To Attention V2-NCn97L5WbCY.en.vtt

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01. 01 Introduction To Attention V2-NCn97L5WbCY.mp4

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01. Introduction to Attention.html

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02. Applications seq2seq-tDJBDwriJYQ.en.vtt

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02. Architecture encoder decoder-dkHdEAJnV_w.en.vtt

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02. Encoders and Decoders.html

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03. Elective Text Sentiment Analysis.html

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04. 02 Sequence To Sequence Recap V2-MRPHIPR0pGE.en.vtt

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04. Sequence to Sequence Recap.html

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05. 03 Attention Overview Encoding V2-IctAnMaVUKc.en.vtt

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05. Encoding -- Attention Overview.html

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06. 04 Attention Overview Decoding V2-DJxiPd585GY.en.vtt

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06. Decoding -- Attention Overview.html

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07. Attention Overview.html

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08. 05 Attention Encoder V2-sphe9LDT4rA.en.vtt

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08. Attention Encoder.html

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09. 06 Attention Decoder V1-5mMz6nN9_Ss.en.vtt

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09. Attention Decoder.html

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11. 07 Additive And Multiplicative Attention V1-2eqIUDjefNg.en.vtt

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11. Bahdanau and Luong Attention.html

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12. 08 Multiplicative Attention V2-1-OwCgrx1eQ.mp4

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12. Multiplicative Attention.html

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13. 09 Additive Attention V2-93VfVWZ-IvY.en.vtt

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13. Additive Attention.html

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14. Additive and Multiplicative Attention.html

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15. 10 Computer Vision Applications V3-bhWwc4BYTYc.en.vtt

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15. Computer Vision Applications.html

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16. 11 Other Attention Methods V2-VmsR9FVpQiM.en.vtt

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16. 11 Other Attention Methods V2-VmsR9FVpQiM.mp4

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16. Other Attention Methods.html

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17. 12 The Transformer And Self Attention V2-F-XN72bQiMQ.en.vtt

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17. 12 The Transformer And Self Attention V2-F-XN72bQiMQ.mp4

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17. The Transformer and Self-Attention.html

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18. Notebook Attention Basics.html

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19. [SOLUTION] Attention Basics.html

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20. Outro.html

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index.html

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/img/

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/Part 02-Module 01-Lesson 07_Image Captioning/

01. 01 L Introduction V3-dobNslC2y-o.en.vtt

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01. 01 L Introduction V3-dobNslC2y-o.mp4

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01. 01 L Introduction V3-dobNslC2y-o.zh-CN.vtt

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01. Introduction to Image Captioning.html

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02. 02 Leveraging Neural Networks V3-thClOUTiNxY.en.vtt

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02. 02 Leveraging Neural Networks V3-thClOUTiNxY.mp4

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02. Leveraging Neural Networks.html

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03. 03 Captions And The COCO Dataset V3-DMmJs1w1n7A.en.vtt

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03. 03 Captions And The COCO Dataset V3-DMmJs1w1n7A.mp4

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03. Captions and the COCO Dataset.html

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04. Visualize the Dataset.html

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05. 04 CNNRNN Model V3-n7kdMiX1Xz8.en.vtt

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05. CNN-RNN Model.html

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06. 05 The Glue V5-u2ZdcUDnHm0.en.vtt

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06. The Glue, Feature Vector.html

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07. 06 Tokenizing Captions V3-aeEFb0eSzJ8.en.vtt

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07. 06 Tokenizing Captions V3-aeEFb0eSzJ8.mp4

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08. Tokenization-4Ieotbeh4u8.en.vtt

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08. Tokenizing Words.html

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09. 07 RNN Training V4-P-tHxD7kRmA.en.vtt

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09. 07 RNN Training V4-P-tHxD7kRmA.mp4

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09. RNN Training.html

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10. 08 Video Captioning V1-I_m9JyKTfbQ.en.vtt

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10. 08 Video Captioning V1-I_m9JyKTfbQ.mp4

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10. Video Captioning.html

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11. 09 On To The Project V2-MsxNRaYTNSk.en.vtt

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11. 09 On To The Project V2-MsxNRaYTNSk.mp4

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11. On to the Project!.html

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index.html

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/img/

screen-shot-2018-05-03-at-3.09.44-pm.png

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screen-shot-2018-05-03-at-3.12.38-pm.png

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screen-shot-2018-05-03-at-3.15.20-pm.png

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/Part 02-Module 01-Lesson 08_Project Image Captioning/

01. Project Overview.html

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02. LSTM InputsOutputs.html

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/Part 02-Module 01-Lesson 09_Optional Cloud Computing with AWS/

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03. Get Access to GPU Instances.html

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index.html

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/Part 03-Module 01-Lesson 01_Introduction to Motion/

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05. 01 Motion Vector V2-I3f3IEUI2tg.en.vtt

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index.html

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/img/

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/Part 03-Module 01-Lesson 02_Robot Localization/

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17. 16 Test Sense Function V1-gytbuOI9-3g.en.vtt

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36. Sense and Move 2.html

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37. Localization Summary.html

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38. C++ Elective Implementation.html

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38. Lesson Overview C++-lR3PH3bL-9U.mp4

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index.html

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/Part 03-Module 01-Lesson 03_Mini-project 2D Histogram Filter/

01. Project Overview-uaWZNKGTtgM.en.vtt

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01. Project Overview-uaWZNKGTtgM.mp4

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01. Project Overview-uaWZNKGTtgM.zh-CN.vtt

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01. Project Overview.html

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02. Notebook - Two Dimensional Histogram Filter.html

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03. Testing on Your Own.html

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index.html

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/Part 03-Module 01-Lesson 04_Introduction to Kalman Filters/

01. Kalman Filters and Linear Algebra.html

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01. 矩阵介绍-Ugx3mldc0lE.en.vtt

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02. Introduction-XZL934YQ-FQ.en.vtt

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04. Answer Tracking Intro.html

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05. Gaussian Intro.html

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10. KALMAN QUIZ Shifting The Mean 01 RENDER 1 V2-gfBdoCFborg.en.vtt

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14. KALMAN QUIZ Parameter Update 01 RENDER V3-UUXETqShme4.en.vtt

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16. Notebook New Mean and Variance.html

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17. New Mean and Variance Solution - Artificial Intelligence for Robotics-SwxRWZaC1FM.en.vtt

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17. New Mean and Variance Solution - Artificial Intelligence for Robotics-SwxRWZaC1FM.zh-CN.vtt

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17. Solution New Mean and Variance.html

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18. KALMAN QUIZ Gaussian Motion 01 RENDER V2-LFPT0R3VaPs.en.vtt

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20. Predict Function.html

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22. Answer Predict Function.html

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23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.en.vtt

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23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.zh-CN.vtt

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23. Kalman Filter Code.html

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24. Notebook 1D Kalman Filter.html

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25. Answer 1D Kalman Filter.html

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26. KALMAN QUIZ Kalman Prediction V1-d8Gx4-RghD0.en.vtt

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27. Next Motion Models and State.html

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/Part 03-Module 01-Lesson 05_Representing State and Motion/

01. Localization Steps-4SiMoSTf4rQ.en.vtt

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01. Localization Steps-4SiMoSTf4rQ.mp4

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02. Intro To State-zfilXYrW4Gk.en.vtt

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03. Motion Models-qSdbn_PVQnk.zh-CN.vtt

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03. Motion Models.html

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04. Quiz Predicting State.html

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05. A Different Model-Mh0g-SMpMI4.en.vtt

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06. Kinematics.html

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07. Quantifying State-9zMbwSqTZAc.en.vtt

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08. Lesson Outline-jh7wLGXrm3E.en.vtt

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09. Always Moving-EQBQlHvxAQA.en.vtt

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10. Car Object-SnfhGZ76h7Y.en.vtt

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10. Car Object-SnfhGZ76h7Y.mp4

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10. Car Object-SnfhGZ76h7Y.zh-CN.vtt

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10. Car Object.html

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11. Interacting with a Car Object.html

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12. Look at the Class Code.html

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13. Turn Right.html

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14. Adding Color.html

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14. Nd113 C2 L3 20 L Adding Color V2-iltQBIpbCSw.en.vtt

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14. Nd113 C2 L3 20 L Adding Color V2-iltQBIpbCSw.zh-CN.vtt

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15. Instantiate Multiple Cars.html

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20. State Transformation Matrix-DRRuQMYo800.en.vtt

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21. Matrix Multiplication.html

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/Part 03-Module 01-Lesson 06_Matrices and Transformation of State/

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01. Kalman Filter Land.html

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02. Kalman Filter Prediction.html

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04. More Kalman FIlters.html

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05. A Note on Notation.html

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06. Kalman Filter Design.html

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07. Let's Look at Where We Are.html

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08. Nd113 C2 L3 08 L The Kalman Filter Equations V2-X9UUpk5URuw.en.vtt

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08. Nd113 C2 L3 08 L The Kalman Filter Equations V2-X9UUpk5URuw.zh-CN.vtt

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08. The Kalman Filter Equations.html

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09. Nd113 C2 L3 09 L Simplifying The Kalman Filter Equations V3-UpC0D-SEtD0.en.vtt

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09. Simplifying the Kalman Filter Equations.html

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10. The Rest of the Lesson.html

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11. Representing State with Matrices.html

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12. Kalman Equation Reference.html

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13. What is a vector.html

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14. Vectors in Python.html

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15. Coding Vectors.html

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16. Coding Vectors (solution).html

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17. Guide to Mathematical Notation.html

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18. Matrices in Python.html

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19. Coding Matrices.html

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20. Coding Matrices (Solution).html

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21. Matrix Addition.html

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22. Coding Matrix Addition.html

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23. Matrix Multiplication.html

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24. Coding Matrix Multiplication.html

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25. Transpose of a Matrix.html

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26. Coding the Transpose.html

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27. The Identity Matrix.html

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28. Coding Identity Matrix.html

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29. Matrix Inverse.html

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31. What to Take Away from this Lesson.html

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index.html

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/Part 03-Module 01-Lesson 07_Simultaneous Localization and Mapping/

01. 02 SLAM V1-UVkkDPJshgM.en.vtt

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01. 02 SLAM V1-UVkkDPJshgM.mp4

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01. 02 SLAM V1-UVkkDPJshgM.zh-CN.vtt

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01. Introduction to SLAM.html

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02. 03 Graph SLAM Quiz V2-OGBC9HrVqd4.en.vtt

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02. Quiz Graph SLAM.html

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03. Answer Graph SLAM.html

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04. 04 Implementing Constraints Quiz V1-2V3ZF08TcX8.en.vtt

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06. 06 Adding Landmarks Quiz V1-_JUCLtoh1CE.en.vtt

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07. Answer Adding Landmarks.html

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08. Quiz Matrix Modification.html

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10. 10 Untouched Fields QUIZ RENDER V2-V9qbWSqqKwg.en.vtt

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12. 12 Omega And Xi QUIZ RENDER V1-KqCBoAaa5jQ.en.vtt

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12. Quiz Omega and Xi.html

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13. Notebook Omega and Xi .html

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14. Quiz Landmark Position.html

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15. Answer Landmark Position.html

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16. Notebook Including Sensor Measurements.html

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18. 16 Introducing Noise Solution RENDER V1-X5kh82oke5w.en.vtt

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18. Answer Introducing Noise.html

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19. 17 Confident Measurements QUIZ RENDER V2-WRANOBm89I4.en.vtt

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19. Confident Measurements.html

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20. Notebook Confident Measurements.html

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21. 18 Implementing SLAM QUIZ RENDER V1-Ptx33qEaUQQ.en.vtt

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21. 18 Implementing SLAM QUIZ RENDER V1-Ptx33qEaUQQ.zh-CN.vtt

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21. SLAM Summary.html

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index.html

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/Part 03-Module 01-Lesson 08_Optional Vehicle Motion and Calculus/

01. Introduction to Odometry.html

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01. 惯性导航-vWgG0d2HOVE.en.vtt

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02. Inertial Navigation Sensors.html

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03. Plotting Position vs. Time.html

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04. Interpreting Position vs. Time Graphs.html

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05. A Typical Calculus Problem.html

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06. How Odometers Work.html

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07. Speed from Position Data.html

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08. Position, Velocity, and Acceleration.html

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09. Implement an Accelerometer.html

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10. Differentiation Recap.html

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11. Acceleration Basics.html

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12. Plotting Elevator Acceleration.html

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13. 06 L Reasoning About Two Peaks H1 V1-bgPS1GO1EJs.en.vtt

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14. 07 L The Integral Area Under A Curve H1 V2-QNvgwoIrsIE.en.vtt

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14. The Integral Area Under a Curve.html

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15. Approximating the Integral.html

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16. Approximating Integrals with Code.html

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17. Integrating Accelerometer Data.html

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18. Rate Gyros.html

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19. Integrating Rate Gyro Data.html

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20. Working with Real Data-WvEcWtAz-OQ.en.vtt

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20. Working with Real Data.html

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21. Accumulating Errors.html

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22. Sensor Strengths and Weaknesses.html

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23. Summary and Back to Trigonometry.html

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24. Trigonometry And Vehicle Motion-WY3T-9GHI_0.en.vtt

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24. Trigonometry And Vehicle Motion-WY3T-9GHI_0.mp4

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25. Solving Trig Problems Part1-qI4i845d7Qg.en.vtt

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25. Solving Trig Problems.html

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26. Keeping Track of x and y.html

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27. Keeping Track of x and y (solution).html

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28. Conclusion.html

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29. Project Overview.html

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30. Lab - Reconstructing Trajectories.html

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/Part 03-Module 01-Lesson 09_Project Landmark Detection Tracking (SLAM)/

01. Project Overview.html

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02. Project Landmark Detection Tracking (SLAM).html

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Project Description - Landmark Detection Tracking (SLAM).html

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Project Rubric - Landmark Detection Tracking (SLAM).html

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/Part 04-Module 01-Lesson 01_Applying Deep Learning Models/

01. Style Transfer.html

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02. DeepTraffic.html

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02. Navegação de tráfico 3-az5ElmV4DhY.en-US.vtt

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03. Flappy Bird.html

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04. Books to Read.html

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index.html

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/Part 05-Module 01-Lesson 01_Feedforward and Backpropagation/

01. Instructor.html

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02. Why Neural Networks-zAkzOZntK6Y.en.vtt

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03. Multiclass Classification-uNTtvxwfox0.en.vtt

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03. Neural Network Architecture.html

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04. Feedforward.html

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05. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt

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index.html

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/Part 05-Module 01-Lesson 02_Training Neural Networks/

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14. Learning Rate Decay.html

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14. Learning Rate-TwJ8aSZoh2U.en.vtt

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14. Learning Rate-TwJ8aSZoh2U.mp4

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15. Momentum-r-rYz_PEWC8.en.vtt

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16. Error Functions Around the World-34AAcTECu2A.en.vtt

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index.html

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/Part 05-Module 01-Lesson 03_Deep Learning with PyTorch/

01. Instructor.html

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02. Introducing PyTorch.html

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03. Part 1 V2-n4mbZYIfKb4.en.vtt

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03. PyTorch Tensors.html

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04. Defining Networks.html

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04. Py Part 2 V1-u50_ZyKqt8g.en.vtt

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05. Training Networks.html

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06. Fashion-MNIST Exercise.html

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06. PyTorch - Part 4-AEJV_RKZ7VU.en.vtt

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07. Inference Validation.html

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08. Saving and Loading Trained Networks.html

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09. Loading Data Sets with Torchvision.html

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09. PyTorch - Part 7-hFu7GTfRWks.en.vtt

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10. Transfer Learning.html

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11. Transfer Learning Solution.html

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index.html

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/Part 06-Module 01-Lesson 01_Deep Learning for Cancer Detection with Sebastian Thrun/

01. Intro.html

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02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt

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02. Skin Cancer.html

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03. Survival Probability of Skin Cancer.html

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04. Medical Classification-RCOSP60dV7U.en.vtt

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06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt

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09. Training The Neural Network-HwiI-UXUx-M.en.vtt

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10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.en.vtt

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12. Validating The Training-Oxm9ofvov3I.en.vtt

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13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt

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13. Quiz Sensitivity and Specificity.html

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15. More on Sensitivity and Specificity.html

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16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt

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17. Solution Diagnosing Cancer.html

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18. Images-1GdiN5Wc8LA.mp4

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18. ROC Curve-2Iw5TiGzJI4.en.vtt

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18. Refresh on ROC Curves.html

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19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt

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19. Quiz ROC Curve.html

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20. Solution ROC Curve.html

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21. Comparing our Results with Doctors.html

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21. ROC Curve-fWwe_JlpnlQ.en.vtt

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21. ROC Curve-fWwe_JlpnlQ.mp4

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22. Visualization-aGIGB4Ta3_A.en.vtt

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22. Visualization.html

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23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt

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24. Refresh on Confusion Matrices.html

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25. Confusion Matrix-3rpN-YYlfes.en.vtt

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25. Confusion Matrix.html

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26. Conclusion-WhpE_8sTt-0.en.vtt

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26. Conclusion.html

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27. Useful Resources.html

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28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt

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28. Mini Project Introduction-Rgf3YVFWl-M.mp4

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28. Mini Project Introduction.html

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29. Mini Project Dermatologist AI.html

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index.html

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cat-1.jpeg

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/Part 07-Module 01-Lesson 01_Sentiment Analysis/

01. Introducing Andrew Trask-ltO71Bm8b3M.en.vtt

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02. Andrew Trask - Intro-da1I0mea1jQ.en-US.vtt

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02. Meet Andrew.html

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03. Materials.html

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05. Framing the Problem-IsTOnkAKaJw.en.vtt

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06. Mini Project 1.html

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09. Mini Project 2.html

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11. Building a Neural Network-aM2k7RTjjJI.en.vtt

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12. Mini Project 3.html

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14. Understanding Neural Noise-ubqhh4Iv7O4.en.vtt

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15. Mini Project 4.html

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16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.en.vtt

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19. Further Noise Reduction-Kl3hWxizKVg.en.vtt

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19. Further Noise Reduction.html

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20. Mini Project 6.html

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22. Analysis What's Going on in the Weights-UHsT35pbpcE.en.vtt

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23. Andrew Trask - Outro-nIF0GLOQglQ.en-US.vtt

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23. Conclusion.html

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/Part 08-Module 01-Lesson 01_Fully-Convolutional Neural Networks Semantic Segmentation/

01. Intro-1sm1EbfilXI.en.vtt

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01. Intro-1sm1EbfilXI.mp4

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01. Intro-1sm1EbfilXI.zh-CN.vtt

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01. Intro.html

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02. Why Fully Convolutional Networks (FCNs) -WQ_YOz1o9GM.en.vtt

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03. Fully Convolutional Networks-_Lh2ozg5yTs.en.vtt

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03. Fully Convolutional Networks-_Lh2ozg5yTs.mp4

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/Part 09-Module 01-Lesson 01_C++ Getting Started/

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/Part 09-Module 01-Lesson 02_C++ Vectors/

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/Part 09-Module 01-Lesson 03_Practical C++/

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/Part 09-Module 01-Lesson 04_C++ Object Oriented Programming/

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04. Using a Class in C++ [Demo].html

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/Part 09-Module 01-Lesson 05_Python and C++ Speed/

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/Part 09-Module 02-Lesson 01_C++ Intro to Optimization/

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/Part 09-Module 02-Lesson 02_C++ Optimization Practice/

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02. Software Development and Optimization.html

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03. Optimization Techniques.html

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04. Dead Code.html

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19. Speed Challenge.html

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/Part 09-Module 02-Lesson 03_Project Optimize Histogram Filter/

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