[ 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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种子哈希：be7d0cc5bf248acce6b80bae91199b4a45ff465e
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收录时间：2022-04-09
最近下载：2025-10-10

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文件列表

  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/001 Data Science in Numpy - Part1 (Code).mp4 123.5 MB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/002 Mocking SimCLR(Code).mp4 60.4 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/005 CutMix (Code).mp4 57.2 MB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/001 Image Search(Basic & Cluster).mp4 56.5 MB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/001 NCE & Memory Bank (Code).mp4 55.8 MB
  • ~Get Your Files Here !/03 Pytorch AutoGrad/001 Pytorch AutoGrad.mp4 47.1 MB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/003 SimClr and Supervised Contrastive Learning (Code).mp4 47.0 MB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/002 Network and Training NPILD & Pearl (Code).mp4 42.2 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/001 Non-Parametric Instance-level Discrimination & Metric learning approach.mp4 41.2 MB
  • ~Get Your Files Here !/01 Introduction/001 Why Should You Take This Course_.mp4 36.4 MB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).mp4 34.1 MB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/003 Basic Image Search (Code).mp4 34.0 MB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/003 Data Science in Pytorch - Part 2(Code).mp4 34.0 MB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/001 Supervised Contrastive Learning.mp4 32.5 MB
  • ~Get Your Files Here !/01 Introduction/003 Applications.mp4 31.9 MB
  • ~Get Your Files Here !/03 Pytorch AutoGrad/002 Custom CNN in Pytorch.mp4 31.7 MB
  • ~Get Your Files Here !/10 PEARL/004 PEARL Results.mp4 31.7 MB
  • ~Get Your Files Here !/15 Bonus Lectures/001 Vissl & Albumentations.mp4 31.6 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/006 RandAugment.mp4 30.2 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/008 Non Parametric Softmax (CrossEntropy) (Code).mp4 30.0 MB
  • ~Get Your Files Here !/12 SimCLR/001 SIMCLR Overview.mp4 28.7 MB
  • ~Get Your Files Here !/10 PEARL/001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.mp4 28.6 MB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/002 Data Science in Pytorch - Part1 (Code).mp4 27.4 MB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/002 Temperature Scaling & soft softmax (code).mp4 27.3 MB
  • ~Get Your Files Here !/08 JigSaw/002 Network and Training process.mp4 23.7 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/002 CutMix Paper Overview.mp4 23.7 MB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/001 SoftMax Think out of the box.mp4 23.3 MB
  • ~Get Your Files Here !/07 Prelearing & UVR by Context Prediction (Theory)/003 Results of UVR by Context Prediction.mp4 23.0 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/007 RandAugment (Code).mp4 23.0 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/001 Why Data Augmentation & History.mp4 22.9 MB
  • ~Get Your Files Here !/10 PEARL/003 PEARL Loss.mp4 21.4 MB
  • ~Get Your Files Here !/01 Introduction/004 Course Structure & Important Notes.mp4 20.7 MB
  • ~Get Your Files Here !/01 Introduction/002 Google Colab Setup.mp4 19.9 MB
  • ~Get Your Files Here !/12 SimCLR/002 SIMCLR & Multiview Batch.mp4 19.0 MB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/005 Cluster Search (Code).mp4 18.4 MB
  • ~Get Your Files Here !/12 SimCLR/003 SimCLR Algorithm and Loss.mp4 17.0 MB
  • ~Get Your Files Here !/08 JigSaw/003 Results of JigSaw.mp4 16.4 MB
  • ~Get Your Files Here !/08 JigSaw/001 Overview of Jigsaw.mp4 15.9 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/003 Results of CutMix.mp4 15.8 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/004 Noise contrastive estimation (NCE) - Part 1.mp4 15.8 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/007 NPILD Result.mp4 15.0 MB
  • ~Get Your Files Here !/15 Bonus Lectures/002 Tips From My Expeience.mp4 14.9 MB
  • ~Get Your Files Here !/15 Bonus Lectures/003 Congratulation & Few More ideas.mp4 14.6 MB
  • ~Get Your Files Here !/07 Prelearing & UVR by Context Prediction (Theory)/002 Overview of Unsupervised Visual Representation Learning by Context Prediction.mp4 14.0 MB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/004 CutMix Algorithm.mp4 12.4 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/002 NPILD Training Process.mp4 11.9 MB
  • ~Get Your Files Here !/10 PEARL/002 PEARL Overview Part 2.mp4 11.6 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/006 NPILD Put it all together.mp4 11.2 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/003 Non Parametric Softmax.mp4 9.8 MB
  • ~Get Your Files Here !/07 Prelearing & UVR by Context Prediction (Theory)/001 Pretext Task.mp4 7.3 MB
  • ~Get Your Files Here !/12 SimCLR/005 Softmax is invariant under translation (Important).mp4 6.2 MB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/005 FULL NCE Loss.mp4 5.3 MB
  • ~Get Your Files Here !/12 SimCLR/004 Training Details.mp4 4.4 MB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/002 Faiss Overview.mp4 4.3 MB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/003 Summery.mp4 3.5 MB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/npild_pearl/npild_pearl.ipynb 827.7 kB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/selfsupcon_supcon/selfsupcon_supcon.ipynb 464.3 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/augmentations/cutmix.ipynb 448.0 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/image_search/faiss.ipynb 326.7 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/augmentations/randaug.ipynb 99.5 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/image_search/basic_search_with_resnet_imagenet.ipynb 71.0 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/image_search/basic_img_search_with_pretraied_resnet_trained_with_cifar10.ipynb 67.7 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/background/torch_training_process.ipynb 65.5 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/image_search/cluster_search_v1.ipynb 61.8 kB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/soft_softmax/not_so_soft.ipynb 54.9 kB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/npild_pearl/mock_npild_pearl.ipynb 36.6 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/background/numpy_v1.ipynb 20.9 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/001 Data Science in Numpy - Part1 (Code).en.srt 17.2 kB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/selfsupcon_supcon/mock_selfsupcon_loss.ipynb 14.5 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/background/torch_v2.ipynb 13.0 kB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/002 Mocking SimCLR(Code).en.srt 11.3 kB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/001 NCE & Memory Bank (Code).en.srt 10.7 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/background/torch_intro.ipynb 9.4 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/005 CutMix (Code).en.srt 9.1 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/003 Data Science in Pytorch - Part 2(Code).en.srt 8.3 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/001 Image Search(Basic & Cluster).en.srt 8.1 kB
  • ~Get Your Files Here !/03 Pytorch AutoGrad/001 Pytorch AutoGrad.en.srt 7.7 kB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/003 SimClr and Supervised Contrastive Learning (Code).en.srt 7.7 kB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/npild_pearl/non_parrametric_softmax_crossentropy.ipynb 7.5 kB
  • ~Get Your Files Here !/10 PEARL/003 PEARL Loss.en.srt 7.0 kB
  • ~Get Your Files Here !/02 Data Science in Numpy & Pytorch (code) - Background/002 Data Science in Pytorch - Part1 (Code).en.srt 6.9 kB
  • ~Get Your Files Here !/03 Pytorch AutoGrad/002 Custom CNN in Pytorch.en.srt 6.8 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/001 Non-Parametric Instance-level Discrimination & Metric learning approach.en.srt 6.8 kB
  • ~Get Your Files Here !/13 SupCon & SimCLR (Code)/001 Supervised Contrastive Learning.en.srt 6.6 kB
  • ~Get Your Files Here !/01 Introduction/001 Why Should You Take This Course_.en.srt 6.5 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/003 Basic Image Search (Code).en.srt 6.3 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/008 Non Parametric Softmax (CrossEntropy) (Code).en.srt 6.1 kB
  • ~Get Your Files Here !/10 PEARL/004 PEARL Results.en.srt 6.1 kB
  • ~Get Your Files Here !/11 PEARL and NPILD (code)/002 Network and Training NPILD & Pearl (Code).en.srt 6.0 kB
  • ~Get Your Files Here !/15 Bonus Lectures/002 Tips From My Expeience.en.srt 6.0 kB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/001 SoftMax Think out of the box.en.srt 5.4 kB
  • ~Get Your Files Here !/08 JigSaw/002 Network and Training process.en.srt 5.3 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/001 Why Data Augmentation & History.en.srt 5.3 kB
  • ~Get Your Files Here !/10 PEARL/001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.en.srt 5.3 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/004 Noise contrastive estimation (NCE) - Part 1.en.srt 5.2 kB
  • ~Get Your Files Here !/07 Prelearing & UVR by Context Prediction (Theory)/003 Results of UVR by Context Prediction.en.srt 5.2 kB
  • ~Get Your Files Here !/15 Bonus Lectures/003 Congratulation & Few More ideas.en.srt 5.0 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/006 RandAugment.en.srt 4.9 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).en.srt 4.8 kB
  • ~Get Your Files Here !/12 SimCLR/001 SIMCLR Overview.en.srt 4.2 kB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/002 Temperature Scaling & soft softmax (code).en.srt 4.2 kB
  • ~Get Your Files Here !/12 SimCLR/002 SIMCLR & Multiview Batch.en.srt 4.2 kB
  • ~Get Your Files Here !/01 Introduction/004 Course Structure & Important Notes.en.srt 4.0 kB
  • ~Get Your Files Here !/10 PEARL/002 PEARL Overview Part 2.en.srt 4.0 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/002 CutMix Paper Overview.en.srt 3.9 kB
  • ~Get Your Files Here !/01 Introduction/003 Applications.en.srt 3.9 kB
  • ~Get Your Files Here !/01 Introduction/002 Google Colab Setup.en.srt 3.8 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/002 NPILD Training Process.en.srt 3.7 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/006 NPILD Put it all together.en.srt 3.7 kB
  • ~Get Your Files Here !/15 Bonus Lectures/001 Vissl & Albumentations.en.srt 3.6 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/007 RandAugment (Code).en.srt 3.5 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/003 Non Parametric Softmax.en.srt 3.5 kB
  • ~Get Your Files Here !/12 SimCLR/003 SimCLR Algorithm and Loss.en.srt 3.5 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/005 Cluster Search (Code).en.srt 3.4 kB
  • ~Get Your Files Here !/07 Prelearing & UVR by Context Prediction (Theory)/001 Pretext Task.en.srt 3.0 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/003 Results of CutMix.en.srt 2.9 kB
  • ~Get Your Files Here !/05 SOTA Data augmentation (Hands On)/004 CutMix Algorithm.en.srt 2.8 kB
  • ~Get Your Files Here !/07 Prelearing & UVR by Context Prediction (Theory)/002 Overview of Unsupervised Visual Representation Learning by Context Prediction.en.srt 2.4 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/007 NPILD Result.en.srt 2.4 kB
  • ~Get Your Files Here !/08 JigSaw/003 Results of JigSaw.en.srt 2.3 kB
  • ~Get Your Files Here !/12 SimCLR/005 Softmax is invariant under translation (Important).en.srt 2.3 kB
  • ~Get Your Files Here !/08 JigSaw/001 Overview of Jigsaw.en.srt 2.1 kB
  • ~Get Your Files Here !/12 SimCLR/004 Training Details.en.srt 1.9 kB
  • ~Get Your Files Here !/09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)/005 FULL NCE Loss.en.srt 1.7 kB
  • ~Get Your Files Here !/04 Faiss & Image Search (Hands on)/002 Faiss Overview.en.srt 1.6 kB
  • ~Get Your Files Here !/06 Softmax think out of the box (Hands On)/003 Summery.en.srt 602 Bytes
  • ~Get Your Files Here !/Bonus Resources.txt 357 Bytes
  • Get Bonus Downloads Here.url 183 Bytes

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