FreeCourseWeb Pytorch Advanced Deep Learning Computer Vision DataAug
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| Torrent Hash : | BE7D0CC5BF248ACCE6B80BAE91199B4A45FF465E |
| Torrent Added : | at Oct. 25, 2023, 7:46 p.m. in Other |
| Torrent Size : | 1.4 GB |
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FreeCourseWeb Pytorch Advanced Deep Learning Computer Vision DataAug
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FreeCourseWeb Pytorch Advanced Deep Learning Computer Vision DataAug
Get Bonus Downloads Here.url -
001 Why Should You Take This Course_.en.srt -
001 Why Should You Take This Course_.mp4 -
002 Google Colab Setup.en.srt -
002 Google Colab Setup.mp4 -
003 Applications.en.srt -
003 Applications.mp4 -
004 Course Structure & Important Notes.en.srt -
004 Course Structure & Important Notes.mp4 -
001 Data Science in Numpy - Part1 (Code).en.srt -
001 Data Science in Numpy - Part1 (Code).mp4 -
002 Data Science in Pytorch - Part1 (Code).en.srt -
002 Data Science in Pytorch - Part1 (Code).mp4 -
003 Data Science in Pytorch - Part 2(Code).en.srt -
003 Data Science in Pytorch - Part 2(Code).mp4 -
numpy_v1.ipynb -
torch_intro.ipynb -
torch_training_process.ipynb -
torch_v2.ipynb -
001 Pytorch AutoGrad.en.srt -
001 Pytorch AutoGrad.mp4 -
002 Custom CNN in Pytorch.en.srt -
002 Custom CNN in Pytorch.mp4 -
001 Image Search(Basic & Cluster).en.srt -
001 Image Search(Basic & Cluster).mp4 -
002 Faiss Overview.en.srt -
002 Faiss Overview.mp4 -
003 Basic Image Search (Code).en.srt -
003 Basic Image Search (Code).mp4 -
004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).en.srt -
004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).mp4 -
005 Cluster Search (Code).en.srt -
005 Cluster Search (Code).mp4 -
basic_img_search_with_pretraied_resnet_trained_with_cifar10.ipynb -
basic_search_with_resnet_imagenet.ipynb -
cluster_search_v1.ipynb -
faiss.ipynb -
001 Why Data Augmentation & History.en.srt -
001 Why Data Augmentation & History.mp4 -
002 CutMix Paper Overview.en.srt -
002 CutMix Paper Overview.mp4 -
003 Results of CutMix.en.srt -
003 Results of CutMix.mp4 -
004 CutMix Algorithm.en.srt -
004 CutMix Algorithm.mp4 -
005 CutMix (Code).en.srt -
005 CutMix (Code).mp4 -
006 RandAugment.en.srt -
006 RandAugment.mp4 -
007 RandAugment (Code).en.srt -
007 RandAugment (Code).mp4 -
cutmix.ipynb -
randaug.ipynb -
001 SoftMax Think out of the box.en.srt -
001 SoftMax Think out of the box.mp4 -
002 Temperature Scaling & soft softmax (code).en.srt -
002 Temperature Scaling & soft softmax (code).mp4 -
003 Summery.en.srt -
003 Summery.mp4 -
not_so_soft.ipynb -
001 Pretext Task.en.srt -
001 Pretext Task.mp4 -
002 Overview of Unsupervised Visual Representation Learning by Context Prediction.en.srt -
002 Overview of Unsupervised Visual Representation Learning by Context Prediction.mp4 -
003 Results of UVR by Context Prediction.en.srt -
003 Results of UVR by Context Prediction.mp4 -
001 Overview of Jigsaw.en.srt -
001 Overview of Jigsaw.mp4 -
002 Network and Training process.en.srt -
002 Network and Training process.mp4 -
003 Results of JigSaw.en.srt -
003 Results of JigSaw.mp4 -
001 Non-Parametric Instance-level Discrimination & Metric learning approach.en.srt -
001 Non-Parametric Instance-level Discrimination & Metric learning approach.mp4 -
002 NPILD Training Process.en.srt -
002 NPILD Training Process.mp4 -
003 Non Parametric Softmax.en.srt -
003 Non Parametric Softmax.mp4 -
004 Noise contrastive estimation (NCE) - Part 1.en.srt -
004 Noise contrastive estimation (NCE) - Part 1.mp4 -
005 FULL NCE Loss.en.srt -
005 FULL NCE Loss.mp4 -
006 NPILD Put it all together.en.srt -
006 NPILD Put it all together.mp4 -
007 NPILD Result.en.srt -
007 NPILD Result.mp4 -
008 Non Parametric Softmax (CrossEntropy) (Code).en.srt -
008 Non Parametric Softmax (CrossEntropy) (Code).mp4 -
001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.en.srt -
001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.mp4 -
002 PEARL Overview Part 2.en.srt -
002 PEARL Overview Part 2.mp4 -
003 PEARL Loss.en.srt -
003 PEARL Loss.mp4 -
004 PEARL Results.en.srt -
004 PEARL Results.mp4 -
001 NCE & Memory Bank (Code).en.srt -
001 NCE & Memory Bank (Code).mp4 -
002 Network and Training NPILD & Pearl (Code).en.srt -
002 Network and Training NPILD & Pearl (Code).mp4 -
mock_npild_pearl.ipynb -
non_parrametric_softmax_crossentropy.ipynb -
npild_pearl.ipynb -
001 SIMCLR Overview.en.srt -
001 SIMCLR Overview.mp4 -
002 SIMCLR & Multiview Batch.en.srt -
002 SIMCLR & Multiview Batch.mp4 -
003 SimCLR Algorithm and Loss.en.srt -
003 SimCLR Algorithm and Loss.mp4 -
004 Training Details.en.srt -
004 Training Details.mp4 -
005 Softmax is invariant under translation (Important).en.srt -
005 Softmax is invariant under translation (Important).mp4 -
001 Supervised Contrastive Learning.en.srt -
001 Supervised Contrastive Learning.mp4 -
002 Mocking SimCLR(Code).en.srt -
002 Mocking SimCLR(Code).mp4 -
003 SimClr and Supervised Contrastive Learning (Code).en.srt -
003 SimClr and Supervised Contrastive Learning (Code).mp4 -
mock_selfsupcon_loss.ipynb -
selfsupcon_supcon.ipynb -
001 Vissl & Albumentations.en.srt -
001 Vissl & Albumentations.mp4 -
002 Tips From My Expeience.en.srt -
002 Tips From My Expeience.mp4 -
003 Congratulation & Few More ideas.en.srt -
003 Congratulation & Few More ideas.mp4 -
Bonus Resources.txt -
Please login or create a FREE account to post comments
Get Bonus Downloads Here.url -
183 bytes
001 Why Should You Take This Course_.en.srt -
6.4 KB
001 Why Should You Take This Course_.mp4 -
34.7 MB
002 Google Colab Setup.en.srt -
3.7 KB
002 Google Colab Setup.mp4 -
18.9 MB
003 Applications.en.srt -
3.8 KB
003 Applications.mp4 -
30.5 MB
004 Course Structure & Important Notes.en.srt -
3.9 KB
004 Course Structure & Important Notes.mp4 -
19.7 MB
001 Data Science in Numpy - Part1 (Code).en.srt -
16.8 KB
001 Data Science in Numpy - Part1 (Code).mp4 -
117.8 MB
002 Data Science in Pytorch - Part1 (Code).en.srt -
6.8 KB
002 Data Science in Pytorch - Part1 (Code).mp4 -
26.1 MB
003 Data Science in Pytorch - Part 2(Code).en.srt -
8.1 KB
003 Data Science in Pytorch - Part 2(Code).mp4 -
32.4 MB
numpy_v1.ipynb -
20.4 KB
torch_intro.ipynb -
9.2 KB
torch_training_process.ipynb -
64.0 KB
torch_v2.ipynb -
12.7 KB
001 Pytorch AutoGrad.en.srt -
7.5 KB
001 Pytorch AutoGrad.mp4 -
44.9 MB
002 Custom CNN in Pytorch.en.srt -
6.7 KB
002 Custom CNN in Pytorch.mp4 -
30.3 MB
001 Image Search(Basic & Cluster).en.srt -
7.9 KB
001 Image Search(Basic & Cluster).mp4 -
53.9 MB
002 Faiss Overview.en.srt -
1.5 KB
002 Faiss Overview.mp4 -
4.1 MB
003 Basic Image Search (Code).en.srt -
6.2 KB
003 Basic Image Search (Code).mp4 -
32.5 MB
004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).en.srt -
4.7 KB
004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).mp4 -
32.6 MB
005 Cluster Search (Code).en.srt -
3.3 KB
005 Cluster Search (Code).mp4 -
17.6 MB
basic_img_search_with_pretraied_resnet_trained_with_cifar10.ipynb -
66.2 KB
basic_search_with_resnet_imagenet.ipynb -
69.4 KB
cluster_search_v1.ipynb -
60.4 KB
faiss.ipynb -
319.0 KB
001 Why Data Augmentation & History.en.srt -
5.1 KB
001 Why Data Augmentation & History.mp4 -
21.8 MB
002 CutMix Paper Overview.en.srt -
3.8 KB
002 CutMix Paper Overview.mp4 -
22.6 MB
003 Results of CutMix.en.srt -
2.8 KB
003 Results of CutMix.mp4 -
15.1 MB
004 CutMix Algorithm.en.srt -
2.7 KB
004 CutMix Algorithm.mp4 -
11.8 MB
005 CutMix (Code).en.srt -
8.9 KB
005 CutMix (Code).mp4 -
54.6 MB
006 RandAugment.en.srt -
4.8 KB
006 RandAugment.mp4 -
28.8 MB
007 RandAugment (Code).en.srt -
3.5 KB
007 RandAugment (Code).mp4 -
21.9 MB
cutmix.ipynb -
437.5 KB
randaug.ipynb -
97.2 KB
001 SoftMax Think out of the box.en.srt -
5.2 KB
001 SoftMax Think out of the box.mp4 -
22.2 MB
002 Temperature Scaling & soft softmax (code).en.srt -
4.1 KB
002 Temperature Scaling & soft softmax (code).mp4 -
26.0 MB
003 Summery.en.srt -
602 bytes
003 Summery.mp4 -
3.3 MB
not_so_soft.ipynb -
53.6 KB
001 Pretext Task.en.srt -
2.9 KB
001 Pretext Task.mp4 -
6.9 MB
002 Overview of Unsupervised Visual Representation Learning by Context Prediction.en.srt -
2.4 KB
002 Overview of Unsupervised Visual Representation Learning by Context Prediction.mp4 -
13.4 MB
003 Results of UVR by Context Prediction.en.srt -
5.0 KB
003 Results of UVR by Context Prediction.mp4 -
22.0 MB
001 Overview of Jigsaw.en.srt -
2.0 KB
001 Overview of Jigsaw.mp4 -
15.1 MB
002 Network and Training process.en.srt -
5.2 KB
002 Network and Training process.mp4 -
22.6 MB
003 Results of JigSaw.en.srt -
2.2 KB
003 Results of JigSaw.mp4 -
15.6 MB
001 Non-Parametric Instance-level Discrimination & Metric learning approach.en.srt -
6.7 KB
001 Non-Parametric Instance-level Discrimination & Metric learning approach.mp4 -
39.3 MB
002 NPILD Training Process.en.srt -
3.6 KB
002 NPILD Training Process.mp4 -
11.3 MB
003 Non Parametric Softmax.en.srt -
3.4 KB
003 Non Parametric Softmax.mp4 -
9.3 MB
004 Noise contrastive estimation (NCE) - Part 1.en.srt -
5.0 KB
004 Noise contrastive estimation (NCE) - Part 1.mp4 -
15.1 MB
005 FULL NCE Loss.en.srt -
1.6 KB
005 FULL NCE Loss.mp4 -
5.1 MB
006 NPILD Put it all together.en.srt -
3.6 KB
006 NPILD Put it all together.mp4 -
10.7 MB
007 NPILD Result.en.srt -
2.4 KB
007 NPILD Result.mp4 -
14.3 MB
008 Non Parametric Softmax (CrossEntropy) (Code).en.srt -
6.0 KB
008 Non Parametric Softmax (CrossEntropy) (Code).mp4 -
28.6 MB
001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.en.srt -
5.1 KB
001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.mp4 -
27.3 MB
002 PEARL Overview Part 2.en.srt -
3.9 KB
002 PEARL Overview Part 2.mp4 -
11.1 MB
003 PEARL Loss.en.srt -
6.8 KB
003 PEARL Loss.mp4 -
20.4 MB
004 PEARL Results.en.srt -
6.0 KB
004 PEARL Results.mp4 -
30.3 MB
001 NCE & Memory Bank (Code).en.srt -
10.5 KB
001 NCE & Memory Bank (Code).mp4 -
53.2 MB
002 Network and Training NPILD & Pearl (Code).en.srt -
5.9 KB
002 Network and Training NPILD & Pearl (Code).mp4 -
40.3 MB
mock_npild_pearl.ipynb -
35.8 KB
non_parrametric_softmax_crossentropy.ipynb -
7.3 KB
npild_pearl.ipynb -
808.3 KB
001 SIMCLR Overview.en.srt -
4.1 KB
001 SIMCLR Overview.mp4 -
27.4 MB
002 SIMCLR & Multiview Batch.en.srt -
4.1 KB
002 SIMCLR & Multiview Batch.mp4 -
18.1 MB
003 SimCLR Algorithm and Loss.en.srt -
3.4 KB
003 SimCLR Algorithm and Loss.mp4 -
16.2 MB
004 Training Details.en.srt -
1.8 KB
004 Training Details.mp4 -
4.2 MB
005 Softmax is invariant under translation (Important).en.srt -
2.2 KB
005 Softmax is invariant under translation (Important).mp4 -
5.9 MB
001 Supervised Contrastive Learning.en.srt -
6.4 KB
001 Supervised Contrastive Learning.mp4 -
31.0 MB
002 Mocking SimCLR(Code).en.srt -
11.0 KB
002 Mocking SimCLR(Code).mp4 -
57.6 MB
003 SimClr and Supervised Contrastive Learning (Code).en.srt -
7.5 KB
003 SimClr and Supervised Contrastive Learning (Code).mp4 -
44.8 MB
mock_selfsupcon_loss.ipynb -
14.2 KB
selfsupcon_supcon.ipynb -
453.4 KB
001 Vissl & Albumentations.en.srt -
3.5 KB
001 Vissl & Albumentations.mp4 -
30.1 MB
002 Tips From My Expeience.en.srt -
5.8 KB
002 Tips From My Expeience.mp4 -
14.2 MB
003 Congratulation & Few More ideas.en.srt -
4.9 KB
003 Congratulation & Few More ideas.mp4 -
13.9 MB
Bonus Resources.txt -
357 bytes
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