| 1 - Course Overview English.srt | 4.9 KB | ||
| 1 - Course Overview.mp4 | 31.5 MB | ||
| 1 - Quiz on Numpy.html | 102.4 B | ||
| 1 - Test Numpy skill.html | 0 B | ||
| 10 - Image SearchBasic & Cluster English.srt | 7.8 KB | ||
| 10 - Image SearchBasic & Cluster.mp4 | 53.9 MB | ||
| 11 - Faiss Overview English.srt | 1.6 KB | ||
| 11 - Faiss Overview.mp4 | 10.2 MB | ||
| 12 - Basic Image Search Code English.srt | 6.5 KB | ||
| 12 - Basic Image Search Code.mp4 | 38.6 MB | ||
| 13 - Basic Image Search With pertained Resnet cifar10 dataset Code English.srt | 4.7 KB | ||
| 13 - Basic Image Search With pertained Resnet cifar10 dataset Code.mp4 | 32.6 MB | ||
| 14 - Cluster Search Code English.srt | 3.5 KB | ||
| 14 - Cluster Search Code.mp4 | 23.7 MB | ||
| 15 - Why Data Augmentation & History English.srt | 5.2 KB | ||
| 15 - Why Data Augmentation & History.mp4 | 26.7 MB | ||
| 16 - CutMix Paper Overview English.srt | 3.8 KB | ||
| 16 - CutMix Paper Overview.mp4 | 28.3 MB | ||
| 17 - Results of CutMix English.srt | 2.8 KB | ||
| 17 - Results of CutMix.mp4 | 15.1 MB | ||
| 18 - CutMix Algorithm English.srt | 2.7 KB | ||
| 18 - CutMix Algorithm.mp4 | 11.8 MB | ||
| 19 - CutMix Code English.srt | 8.8 KB | ||
| 19 - CutMix Code.mp4 | 54.6 MB | ||
| 2 - Applications English.srt | 4.1 KB | ||
| 2 - Applications.mp4 | 38.3 MB | ||
| 2 - Test what you have learned.html | 0 B | ||
| 20 - RandAugment English.srt | 4.8 KB | ||
| 20 - RandAugment.mp4 | 28.8 MB | ||
| 21 - RandAugment Code English.srt | 3.4 KB | ||
| 21 - RandAugment Code.mp4 | 21.9 MB | ||
| 22 - SoftMax Think out of the box English.srt | 5.6 KB | ||
| 22 - SoftMax Think out of the box.mp4 | 27.7 MB | ||
| 23 - Temperature Scaling & soft softmax code English.srt | 4.3 KB | ||
| 23 - Temperature Scaling & soft softmax code.mp4 | 31.3 MB | ||
| 24 - Summery English.srt | 614.4 B | ||
| 24 - Summery.mp4 | 9.3 MB | ||
| 25 - Pretext Task English.srt | 2.9 KB | ||
| 25 - Pretext Task.mp4 | 6.9 MB | ||
| 26 - Overview of Unsupervised Visual Representation Learning by Context Prediction English.srt | 2.4 KB | ||
| 26 - Overview of Unsupervised Visual Representation Learning by Context Prediction.mp4 | 19.1 MB | ||
| 27 - Results of UVR by Context Prediction English.srt | 5 KB | ||
| 27 - Results of UVR by Context Prediction.mp4 | 22 MB | ||
| 28 - Overview of Jigsaw English.srt | 2 KB | ||
| 28 - Overview of Jigsaw.mp4 | 15.1 MB | ||
| 29 - Network and Training process English.srt | 5.1 KB | ||
| 29 - Network and Training process.mp4 | 22.6 MB | ||
| 3 - Google Colab Setup English.srt | 3.7 KB | ||
| 3 - Google Colab Setup.mp4 | 18.9 MB | ||
| 30 - Results of JigSaw English.srt | 2.2 KB | ||
| 30 - Results of JigSaw.mp4 | 15.6 MB | ||
| 31 - NonParametric Instancelevel Discrimination & Metric learning approach English.srt | 6.6 KB | ||
| 31 - NonParametric Instancelevel Discrimination & Metric learning approach.mp4 | 39.3 MB | ||
| 32 - NPILD Training Process English.srt | 3.6 KB | ||
| 32 - NPILD Training Process.mp4 | 11.3 MB | ||
| 33 - Non Parametric Softmax English.srt | 3.4 KB | ||
| 33 - Non Parametric Softmax.mp4 | 9.3 MB | ||
| 34 - Noise contrastive estimation NCE Part 1 English.srt | 5 KB | ||
| 34 - Noise contrastive estimation NCE Part 1.mp4 | 15.1 MB | ||
| 35 - FULL NCE Loss English.srt | 1.6 KB | ||
| 35 - FULL NCE Loss.mp4 | 5.1 MB | ||
| 36 - NPILD Put it all together English.srt | 3.6 KB | ||
| 36 - NPILD Put it all together.mp4 | 10.7 MB | ||
| 37 - NPILD Result English.srt | 2.3 KB | ||
| 37 - NPILD Result.mp4 | 14.3 MB | ||
| 38 - Non Parametric Softmax CrossEntropy Code English.srt | 5.9 KB | ||
| 38 - Non Parametric Softmax CrossEntropy Code.mp4 | 28.6 MB | ||
| 39 - SelfSupervised Learning of PretextInvariant Representations PEARL Part 1 English.srt | 5.1 KB | ||
| 39 - SelfSupervised Learning of PretextInvariant Representations PEARL Part 1.mp4 | 27.3 MB | ||
| 4 - Course Structure & Important Notes English.srt | 3.9 KB | ||
| 4 - Course Structure & Important Notes.mp4 | 19.7 MB | ||
| 40 - PEARL Overview Part 2 English.srt | 3.9 KB | ||
| 40 - PEARL Overview Part 2.mp4 | 11.1 MB | ||
| 41 - PEARL Loss English.srt | 6.8 KB | ||
| 41 - PEARL Loss.mp4 | 20.4 MB | ||
| 42 - PEARL Results English.srt | 5.9 KB | ||
| 42 - PEARL Results.mp4 | 30.3 MB | ||
| 43 - NCE & Memory Bank Code English.srt | 10.4 KB | ||
| 43 - NCE & Memory Bank Code.mp4 | 53.2 MB | ||
| 44 - Network and Training NPILD & Pearl Code English.srt | 5.8 KB | ||
| 44 - Network and Training NPILD & Pearl Code.mp4 | 40.3 MB | ||
| 45 - SIMCLR Overview English.srt | 4.1 KB | ||
| 45 - SIMCLR Overview.mp4 | 27.4 MB | ||
| 46 - SIMCLR & Multiview Batch English.srt | 4 KB | ||
| 46 - SIMCLR & Multiview Batch.mp4 | 18.1 MB | ||
| 47 - SimCLR Algorithm and Loss English.srt | 3.4 KB | ||
| 47 - SimCLR Algorithm and Loss.mp4 | 16.2 MB | ||
| 48 - Training Details English.srt | 1.8 KB | ||
| 48 - Training Details.mp4 | 4.2 MB | ||
| 49 - Softmax is invariant under translation Important English.srt | 2.3 KB | ||
| 49 - Softmax is invariant under translation Important.mp4 | 11.6 MB | ||
| 5 - Data Science in Numpy Part1 Code English.srt | 13.1 KB | ||
| 5 - Data Science in Numpy Part1 Code.mp4 | 59.1 MB | ||
| 50 - Supervised Contrastive Learning English.srt | 6.4 KB | ||
| 50 - Supervised Contrastive Learning.mp4 | 31 MB | ||
| 51 - Mocking SimCLRCode English.srt | 10.9 KB | ||
| 51 - Mocking SimCLRCode.mp4 | 57.6 MB | ||
| 52 - SimClr and Supervised Contrastive Learning Code English.srt | 7.4 KB | ||
| 52 - SimClr and Supervised Contrastive Learning Code.mp4 | 44.8 MB | ||
| 53 - Vissl & Albumentations English.srt | 3.5 KB | ||
| 53 - Vissl & Albumentations.mp4 | 30.1 MB | ||
| 54 - Tips From My Expeience English.srt | 5.8 KB | ||
| 54 - Tips From My Expeience.mp4 | 14.2 MB | ||
| 55 - Few More ideas English.srt | 4.8 KB | ||
| 55 - Few More ideas.mp4 | 13.9 MB | ||
| 56 - Introduction to DeepFake & Beyond English.srt | 6.1 KB | ||
| 56 - Introduction to DeepFake & Beyond.mp4 | 38.7 MB | ||
| 57 - Generative Vs Discriminative AI With VAE Example will be separate course English.srt | 7.3 KB | ||
| 57 - Generative Vs Discriminative AI With VAE Example will be separate course.mp4 | 50.6 MB | ||
| 6 - Data Science in Pytorch Part1 Code English.srt | 7 KB | ||
| 6 - Data Science in Pytorch Part1 Code.mp4 | 31.5 MB | ||
| 7 - Data Science in Pytorch Part 2Code English.srt | 8.2 KB | ||
| 7 - Data Science in Pytorch Part 2Code.mp4 | 38.1 MB | ||
| 8 - Pytorch AutoGrad English.srt | 7.7 KB | ||
| 8 - Pytorch AutoGrad.mp4 | 51.1 MB | ||
| 8 - torch-grad.ipynb | 8.1 KB | ||
| 9 - Custom CNN in Pytorch English.srt | 7 KB | ||
| 9 - Custom CNN in Pytorch.mp4 | 36.4 MB | ||
| 9 - custom-CNN.ipynb | 120.6 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| 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 | ||
| cutmix.ipynb | 437.5 KB | ||
| faiss.ipynb | 319 KB | ||
| mock_npild_pearl.ipynb | 35.8 KB | ||
| mock_selfsupcon_loss.ipynb | 14.2 KB | ||
| non_parrametric_softmax_crossentropy.ipynb | 7.3 KB | ||
| not_so_soft.ipynb | 53.6 KB | ||
| npild_pearl.ipynb | 808.3 KB | ||
| numpy_v1.ipynb | 20.4 KB | ||
| randaug.ipynb | 97.2 KB | ||
| selfsupcon_supcon.ipynb | 453.4 KB | ||
| torch_intro.ipynb | 9.2 KB | ||
| torch_training_process.ipynb | 64 KB | ||
| torch_v2.ipynb | 12.7 KB | ||
| ▲ 137 total files | |||
Advanced Computer Vision Replearning, Vae, Gan, Deepfake +
https://DevCourseWeb.com
Last updated 12/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.48 GB | Duration: 3h 38m
Advanced CV Deep Representation Learning, Transformer, Data Augmentation VAE, GAN, DEEPFAKE +More in Pytorch & Numpy
What you'll learn
Representation Learning
Deep Unsupervised/Supervised/Self Supervised Visual Representation Learning Techniques
Industry Level Advanced Computer Vision
Awesome SOTA Data Augmentation techniques in pytorch
Various properties of Softmax and CrossEntropy in Numpy & Pytorch
State of the art methods like RandAug, JigSaw, PEARL, NPILD, SimCLR, SupCon and many more..
SimCLR (Simple Contrastive Learning), Supervised contrastive learning
Faiss Search, Image Search and Cluster Search
noise contrastive estimator
Visual Transformers
AutoEncoders, VAE, GAN
DeepFake
Requirements
Desire to learn something awesome and new!
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 540.8 MB | freecoursewb | 3 days | 56 | 19 | |
| 261.4 MB | freecoursewb | 3 days | 0 | 0 | |
| 995.1 MB | freecoursewb | 4 days | 5 | 5 | |
| 1.9 GB | freecoursewb | 4 days | 1 | 9 | |
| 721.7 MB | freecoursewb | 4 days | 0 | 0 |
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