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Udacity Computer Vision Nanodegree v1.0.0

01. 02 Intro To CNN Layers V1 RENDER V3-hT6zBYCuAfw
03. 03 Data And Lesson Outline RENDER V2-jPr-5aZA6NE
05. Convolutional Layers (Part 2)-LX-yVob3c28
09. Pooling Layers-OkkIZNs7Cyc
16. Dropout-Ty6K6YiGdBs
16. ??-r-rYz_PEWC8
19. 04 Feature Visualization V1 RENDER V2-xwGa7RFg1EQ
20. 05 Feature Maps V1RENDER V3-oRhsJHHWtu8
21. 06 First Convolutional Layer T1 V1 RENDER V2-hIHDMWVSfsM
23. 10 Visualizing Activations V1 RENDER V2-CJLNTOXqt3I
28. 20 Summary Of Feature Viz V2 RENDER V2-r2LBoEkXskU
01. 01 CNNs And Scene Understanding RENDER Full V2-_iRqSOsTBQU
02. 02 More Than Classification RENDER V2-vBE5KvvAYzg
03. 03 Classification And Localization RENDER V3-UqNg9d6cKQU
04. 04 Bounding Boxes And Regression V1 RENDER V3-2YM82c7SaCo
06. 05 Region Proposals V1 RENDER V2-HLwpr7h3rPY
07. 06 RCNN V1 RENDER V2-EchapZJMTYU
08. 07 Fast RCNN V1 RENDER V2-6FOBZ9OgWlY
09. 08 Faster RCNN V1 RENDER V2-ySh_Q3KTTBY
10. 09 Detection Without Proposals Summary V1-IMnt1HFu_nc
01. 01 Introduction V3-uyefSrHZesY
02. 02 YOLO Output V2-MyOuuwk0qC4
03. 03 A Convolutional Approach To Sliding Windows V3-8qYqqibIz90
05. 04 Using A Grid To Improve Localization V2-OmgR35Go79Y
06. 05 Training On A Grid V2-uhefpakvXh8
07. 06 Generating Bounding Boxes V3-TGfPX-XcyOs
09. 07 Too Many Boxes V2-nYDWsFdFnQ8
10. 08 Intersection Over Union IOU V1-ieKEHlEjIsY
12. 09 NonMaximal Suppression V1-TE6M29Jo9hk
13. 10 Anchor Boxes V3-IzILYgVb76g
14. 11 YOLO Algorithm V3-ZbQzCHQ8YEo
02. 01 RNN Intro V6 Final-AIQEqg6F38A
03. 02 RNN History V4 Final-HbxAnYUfRnc
04. 03 RNN Applications V3 Final-6JbTNARuKII
05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0
05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc
06. 06 FeedForward A V7 Final-4rCfnWbx8-0
06. 07 FeedForward B V3-kTYbTVh1d0k
08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg
08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY
09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg
09. Regra da cadeia-YAhIBOnbt54
10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA
12. 14 RNN A V4 Final-ofbnDxGSUcg
13. 16 RNN B V4 Final-wsif3p5t7CI
14. 17 RNN Unfolded V3 Final-xLIA_PTWXog
16. 18 RNN Example V5 Final-MDLk3fhpTx0
17. 19 RNN BPTT A V6 Final-eE2L3-2wKac
18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA
19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M
24. RNN Summary-nXP0oGGRrO8
25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY
02. RNN Vs LSTM-70MgF-IwAr8
03. LSTM Basics-gjb68a4XsqE
04. LSTM Architecture-ycwthhdx8ws
06. Learn Gate-aVHVI7ovbHY
07. Forget Gate-iWxpfxLUPSU
08. Remember Gate-0qlm86HaXuU
09. LSTM 7 Use Gate-5Ifolm1jTdY
10. Putting It All Together-IF8FlKW-Zo0
13. Character-Wise RNN-m_S2hs6-j5w
14. Sequence Batching-pdSr5F-9qE0
16. Other Architectures-MsxFDuYlTuQ
02. Introduction-erwnzFD7AeE
03. Learning Rate-HLMjeDez7ps
05. Minibatch Size-GrrO1NFxaW8
06. Number Of Iterations-TTdHpSb4DV8
07. Number Of Hidden Units Layers-IkGAIQH5wH8
08. RNN Hyperparameters-yQvnv7l_aUo
01. 01 Introduction To Attention V2-NCn97L5WbCY
02. Applications seq2seq-tDJBDwriJYQ
02. Architecture encoder decoder-dkHdEAJnV_w
04. 02 Sequence To Sequence Recap V2-MRPHIPR0pGE
05. 03 Attention Overview Encoding V2-IctAnMaVUKc
06. 04 Attention Overview Decoding V2-DJxiPd585GY
08. 05 Attention Encoder V2-sphe9LDT4rA
09. 06 Attention Decoder V1-5mMz6nN9_Ss
11. 07 Additive And Multiplicative Attention V1-2eqIUDjefNg
12. 08 Multiplicative Attention V2-1-OwCgrx1eQ
13. 09 Additive Attention V2-93VfVWZ-IvY
15. 10 Computer Vision Applications V3-bhWwc4BYTYc
16. 11 Other Attention Methods V2-VmsR9FVpQiM
17. 12 The Transformer And Self Attention V2-F-XN72bQiMQ
01. 01 L Introduction V3-dobNslC2y-o
02. 02 Leveraging Neural Networks V3-thClOUTiNxY
03. 03 Captions And The COCO Dataset V3-DMmJs1w1n7A
05. 04 CNNRNN Model V3-n7kdMiX1Xz8
06. 05 The Glue V5-u2ZdcUDnHm0
07. 06 Tokenizing Captions V3-aeEFb0eSzJ8
08. Tokenization-4Ieotbeh4u8
09. 07 RNN Training V4-P-tHxD7kRmA
10. 08 Video Captioning V1-I_m9JyKTfbQ
11. 09 On To The Project V2-MsxNRaYTNSk
01. Object Tracking V4 (1)-ciEo6PyMTeM
02. Localization V1 (1)-QkVyEMTUkAw

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