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[ FreeCourseWeb com ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)

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[ FreeCourseWeb com ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)

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Name:[ FreeCourseWeb com ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)

Infohash: BE7D0CC5BF248ACCE6B80BAE91199B4A45FF465E

Total Size: 1.37 GB

Seeds: 0

Leechers: 0

Stream: Watch Full Movie @ Movie4u

Last Updated: 2021-12-01 04:37:35 (Update Now)

Torrent added: 2021-07-10 20:02:16






Torrent Files List


Get Bonus Downloads Here.url (Size: 1.37 GB) (Files: 128)

 Get Bonus Downloads Here.url

0.18 KB

 ~Get Your Files Here !

  01 Introduction

   001 Why Should You Take This Course_.en.srt

6.36 KB

   001 Why Should You Take This Course_.mp4

34.70 MB

   002 Google Colab Setup.en.srt

3.73 KB

   002 Google Colab Setup.mp4

18.93 MB

   003 Applications.en.srt

3.77 KB

   003 Applications.mp4

30.47 MB

   004 Course Structure & Important Notes.en.srt

3.92 KB

   004 Course Structure & Important Notes.mp4

19.73 MB

  02 Data Science in Numpy & Pytorch (code) - Background

   001 Data Science in Numpy - Part1 (Code).en.srt

16.80 KB

   001 Data Science in Numpy - Part1 (Code).mp4

117.82 MB

   002 Data Science in Pytorch - Part1 (Code).en.srt

6.75 KB

   002 Data Science in Pytorch - Part1 (Code).mp4

26.14 MB

   003 Data Science in Pytorch - Part 2(Code).en.srt

8.07 KB

   003 Data Science in Pytorch - Part 2(Code).mp4

32.41 MB

   background

    numpy_v1.ipynb

20.44 KB

    torch_intro.ipynb

9.20 KB

    torch_training_process.ipynb

63.99 KB

    torch_v2.ipynb

12.74 KB

  03 Pytorch AutoGrad

   001 Pytorch AutoGrad.en.srt

7.51 KB

   001 Pytorch AutoGrad.mp4

44.94 MB

   002 Custom CNN in Pytorch.en.srt

6.68 KB

   002 Custom CNN in Pytorch.mp4

30.27 MB

  04 Faiss & Image Search (Hands on)

   001 Image Search(Basic & Cluster).en.srt

7.92 KB

   001 Image Search(Basic & Cluster).mp4

53.89 MB

   002 Faiss Overview.en.srt

1.54 KB

   002 Faiss Overview.mp4

4.13 MB

   003 Basic Image Search (Code).en.srt

6.19 KB

   003 Basic Image Search (Code).mp4

32.47 MB

   004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).en.srt

4.71 KB

   004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).mp4

32.56 MB

   005 Cluster Search (Code).en.srt

3.27 KB

   005 Cluster Search (Code).mp4

17.55 MB

   image_search

    basic_img_search_with_pretraied_resnet_trained_with_cifar10.ipynb

66.16 KB

    basic_search_with_resnet_imagenet.ipynb

69.38 KB

    cluster_search_v1.ipynb

60.38 KB

    faiss.ipynb

319.01 KB

  05 SOTA Data augmentation (Hands On)

   001 Why Data Augmentation & History.en.srt

5.14 KB

   001 Why Data Augmentation & History.mp4

21.82 MB

   002 CutMix Paper Overview.en.srt

3.78 KB

   002 CutMix Paper Overview.mp4

22.62 MB

   003 Results of CutMix.en.srt

2.82 KB

   003 Results of CutMix.mp4

15.09 MB

   004 CutMix Algorithm.en.srt

2.74 KB

   004 CutMix Algorithm.mp4

11.84 MB

   005 CutMix (Code).en.srt

8.88 KB

   005 CutMix (Code).mp4

54.55 MB

   006 RandAugment.en.srt

4.83 KB

   006 RandAugment.mp4

28.79 MB

   007 RandAugment (Code).en.srt

3.46 KB

   007 RandAugment (Code).mp4

21.89 MB

   augmentations

    cutmix.ipynb

437.48 KB

    randaug.ipynb

97.22 KB

  06 Softmax think out of the box (Hands On)

   001 SoftMax Think out of the box.en.srt

5.23 KB

   001 SoftMax Think out of the box.mp4

22.21 MB

   002 Temperature Scaling & soft softmax (code).en.srt

4.10 KB

   002 Temperature Scaling & soft softmax (code).mp4

26.00 MB

   003 Summery.en.srt

0.59 KB

   003 Summery.mp4

3.29 MB

   soft_softmax

    not_so_soft.ipynb

53.60 KB

  07 Prelearing & UVR by Context Prediction (Theory)

   001 Pretext Task.en.srt

2.92 KB

   001 Pretext Task.mp4

6.94 MB

   002 Overview of Unsupervised Visual Representation Learning by Context Prediction.en.srt

2.39 KB

   002 Overview of Unsupervised Visual Representation Learning by Context Prediction.mp4

13.40 MB

   003 Results of UVR by Context Prediction.en.srt

5.04 KB

   003 Results of UVR by Context Prediction.mp4

21.97 MB

  08 JigSaw

   001 Overview of Jigsaw.en.srt

2.04 KB

   001 Overview of Jigsaw.mp4

15.14 MB

   002 Network and Training process.en.srt

5.16 KB

   002 Network and Training process.mp4

22.65 MB

   003 Results of JigSaw.en.srt

2.21 KB

   003 Results of JigSaw.mp4

15.64 MB

  09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)

   001 Non-Parametric Instance-level Discrimination & Metric learning approach.en.srt

6.68 KB

   001 Non-Parametric Instance-level Discrimination & Metric learning approach.mp4

39.30 MB

   002 NPILD Training Process.en.srt

3.62 KB

   002 NPILD Training Process.mp4

11.30 MB

   003 Non Parametric Softmax.en.srt

3.42 KB

   003 Non Parametric Softmax.mp4

9.30 MB

   004 Noise contrastive estimation (NCE) - Part 1.en.srt

5.04 KB

   004 Noise contrastive estimation (NCE) - Part 1.mp4

15.08 MB

   005 FULL NCE Loss.en.srt

1.62 KB

   005 FULL NCE Loss.mp4

5.07 MB

   006 NPILD Put it all together.en.srt

3.62 KB

   006 NPILD Put it all together.mp4

10.72 MB

   007 NPILD Result.en.srt

2.35 KB

   007 NPILD Result.mp4

14.29 MB

   008 Non Parametric Softmax (CrossEntropy) (Code).en.srt

5.97 KB

   008 Non Parametric Softmax (CrossEntropy) (Code).mp4

28.62 MB

  10 PEARL

   001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.en.srt

5.14 KB

   001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.mp4

27.29 MB

   002 PEARL Overview Part 2.en.srt

3.89 KB

   002 PEARL Overview Part 2.mp4

11.11 MB

   003 PEARL Loss.en.srt

6.85 KB

   003 PEARL Loss.mp4

20.43 MB

   004 PEARL Results.en.srt

5.95 KB

   004 PEARL Results.mp4

30.27 MB

  11 PEARL and NPILD (code)

   001 NCE & Memory Bank (Code).en.srt

10.45 KB

   001 NCE & Memory Bank (Code).mp4

53.20 MB

   002 Network and Training NPILD & Pearl (Code).en.srt

5.86 KB

   002 Network and Training NPILD & Pearl (Code).mp4

40.29 MB

   npild_pearl

    mock_npild_pearl.ipynb

35.78 KB

    non_parrametric_softmax_crossentropy.ipynb

7.29 KB

    npild_pearl.ipynb

808.32 KB

  12 SimCLR

   001 SIMCLR Overview.en.srt

4.14 KB

   001 SIMCLR Overview.mp4

27.41 MB

   002 SIMCLR & Multiview Batch.en.srt

4.06 KB

   002 SIMCLR & Multiview Batch.mp4

18.11 MB

   003 SimCLR Algorithm and Loss.en.srt

3.38 KB

   003 SimCLR Algorithm and Loss.mp4

16.21 MB

   004 Training Details.en.srt

1.82 KB

   004 Training Details.mp4

4.21 MB

   005 Softmax is invariant under translation (Important).en.srt

2.20 KB

   005 Softmax is invariant under translation (Important).mp4

5.94 MB

  13 SupCon & SimCLR (Code)

   001 Supervised Contrastive Learning.en.srt

6.41 KB

   001 Supervised Contrastive Learning.mp4

31.02 MB

   002 Mocking SimCLR(Code).en.srt

11.04 KB

   002 Mocking SimCLR(Code).mp4

57.61 MB

   003 SimClr and Supervised Contrastive Learning (Code).en.srt

7.48 KB

   003 SimClr and Supervised Contrastive Learning (Code).mp4

44.83 MB

   selfsupcon_supcon

    mock_selfsupcon_loss.ipynb

14.16 KB

    selfsupcon_supcon.ipynb

453.38 KB

  15 Bonus Lectures

   001 Vissl & Albumentations.en.srt

3.50 KB

   001 Vissl & Albumentations.mp4

30.09 MB

   002 Tips From My Expeience.en.srt

5.81 KB

   002 Tips From My Expeience.mp4

14.23 MB

   003 Congratulation & Few More ideas.en.srt

4.85 KB

   003 Congratulation & Few More ideas.mp4

13.88 MB

  Bonus Resources.txt

0.35 KB
 

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