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Category:
Language:
English
Total Size:
1.4 GB
Info Hash:
BE7D0CC5BF248ACCE6B80BAE91199B4A45FF465E
Added By:
Added:
Oct. 25, 2023, 7:46 p.m.
Stats:
|
(Last updated: May 14, 2025, 6:02 p.m.)
| File | Size |
|---|---|
| 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 |
Name
DL
Uploader
Size
S/L
Added
-
256.5 MB
[0
/
8]
2023-10-28
| Uploaded by freecoursewb | Size 256.5 MB | Health [ 0 /8 ] | Added 2023-10-28 |
-
304.3 MB
[0
/
3]
2023-06-02
| Uploaded by freecoursewb | Size 304.3 MB | Health [ 0 /3 ] | Added 2023-06-02 |
NOTE
SOURCE: FreeCourseWeb Pytorch Advanced Deep Learning Computer Vision DataAug
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COVER
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