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001 Data Science in Numpy - Part1 (Code).en.srt
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001 Data Science in Numpy - Part1 (Code).mp4
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001 Image Search(Basic & Cluster).en.srt
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001 Image Search(Basic & Cluster).mp4
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001 NCE & Memory Bank (Code).en.srt
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001 NCE & Memory Bank (Code).mp4
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001 Non-Parametric Instance-level Discrimination & Metric learning approach.en.srt
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001 Non-Parametric Instance-level Discrimination & Metric learning approach.mp4
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001 Overview of Jigsaw.en.srt
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001 Overview of Jigsaw.mp4
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001 Pretext Task.en.srt
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001 Pretext Task.mp4
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001 Pytorch AutoGrad.en.srt
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001 SIMCLR Overview.en.srt
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001 SIMCLR Overview.mp4
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001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.en.srt
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001 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1.mp4
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001 SoftMax Think out of the box.en.srt
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001 SoftMax Think out of the box.mp4
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001 Supervised Contrastive Learning.en.srt
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001 Vissl & Albumentations.en.srt
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001 Why Data Augmentation & History.en.srt
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001 Why Data Augmentation & History.mp4
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001 Why Should You Take This Course_.en.srt
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001 Why Should You Take This Course_.mp4
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002 Custom CNN in Pytorch.en.srt
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002 Custom CNN in Pytorch.mp4
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002 CutMix Paper Overview.en.srt
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002 CutMix Paper Overview.mp4
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002 Data Science in Pytorch - Part1 (Code).en.srt
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002 Data Science in Pytorch - Part1 (Code).mp4
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002 Faiss Overview.en.srt
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002 Faiss Overview.mp4
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002 Google Colab Setup.en.srt
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002 Google Colab Setup.mp4
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002 Mocking SimCLR(Code).en.srt
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002 Mocking SimCLR(Code).mp4
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002 NPILD Training Process.en.srt
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002 NPILD Training Process.mp4
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002 Network and Training NPILD & Pearl (Code).en.srt
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002 Network and Training NPILD & Pearl (Code).mp4
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002 Network and Training process.en.srt
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002 Network and Training process.mp4
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002 Overview of Unsupervised Visual Representation Learning by Context Prediction.en.srt
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002 Overview of Unsupervised Visual Representation Learning by Context Prediction.mp4
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002 PEARL Overview Part 2.en.srt
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002 SIMCLR & Multiview Batch.en.srt
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002 SIMCLR & Multiview Batch.mp4
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002 Temperature Scaling & soft softmax (code).en.srt
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002 Temperature Scaling & soft softmax (code).mp4
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002 Tips From My Expeience.en.srt
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003 Applications.en.srt
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003 Applications.mp4
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003 Basic Image Search (Code).en.srt
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003 Basic Image Search (Code).mp4
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003 Congratulation & Few More ideas.en.srt
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003 Data Science in Pytorch - Part 2(Code).en.srt
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003 Non Parametric Softmax.en.srt
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003 Non Parametric Softmax.mp4
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003 PEARL Loss.en.srt
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003 PEARL Loss.mp4
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003 Results of CutMix.en.srt
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003 Results of CutMix.mp4
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003 Results of JigSaw.en.srt
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003 Results of JigSaw.mp4
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003 Results of UVR by Context Prediction.en.srt
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003 SimCLR Algorithm and Loss.en.srt
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003 SimCLR Algorithm and Loss.mp4
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003 SimClr and Supervised Contrastive Learning (Code).en.srt
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003 SimClr and Supervised Contrastive Learning (Code).mp4
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003 Summery.en.srt
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003 Summery.mp4
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004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).en.srt
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004 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code).mp4
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004 Course Structure & Important Notes.en.srt
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004 Course Structure & Important Notes.mp4
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004 CutMix Algorithm.en.srt
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004 CutMix Algorithm.mp4
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004 Noise contrastive estimation (NCE) - Part 1.en.srt
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004 PEARL Results.en.srt
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004 PEARL Results.mp4
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004 Training Details.en.srt
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004 Training Details.mp4
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005 Cluster Search (Code).en.srt
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005 Cluster Search (Code).mp4
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005 CutMix (Code).en.srt
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005 CutMix (Code).mp4
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005 FULL NCE Loss.en.srt
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005 Softmax is invariant under translation (Important).en.srt
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006 NPILD Put it all together.en.srt
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006 NPILD Put it all together.mp4
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006 RandAugment.en.srt
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007 NPILD Result.en.srt
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007 NPILD Result.mp4
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007 RandAugment (Code).en.srt
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007 RandAugment (Code).mp4
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008 Non Parametric Softmax (CrossEntropy) (Code).en.srt
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008 Non Parametric Softmax (CrossEntropy) (Code).mp4
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Bonus Resources.txt
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Get Bonus Downloads Here.url
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basic_img_search_with_pretraied_resnet_trained_with_cifar10.ipynb
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basic_search_with_resnet_imagenet.ipynb
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cluster_search_v1.ipynb
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cutmix.ipynb
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faiss.ipynb
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mock_npild_pearl.ipynb
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mock_selfsupcon_loss.ipynb
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non_parrametric_softmax_crossentropy.ipynb
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not_so_soft.ipynb
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npild_pearl.ipynb
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numpy_v1.ipynb
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randaug.ipynb
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selfsupcon_supcon.ipynb
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torch_intro.ipynb
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torch_training_process.ipynb
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torch_v2.ipynb
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![[ FreeCourseWeb ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)](/static/img/no-torrent-image.png "[ FreeCourseWeb ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)")
