[ FreeCourseWeb ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)

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[ FreeCourseWeb.com ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 Introduction
    • 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)
    02 Data Science in Numpy & Pytorch (code) - Background
    • 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)
      • background
      • 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)
      03 Pytorch AutoGrad
      • 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)
      04 Faiss & Image Search (Hands on)
      • 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)
        • image_search
        • 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)
        05 SOTA Data augmentation (Hands On)
        • 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)
          • augmentations
          • cutmix.ipynb (437.5 KB)
          • randaug.ipynb (97.2 KB)
          06 Softmax think out of the box (Hands On)
          • 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 (0.6 KB)
          • 003 Summery.mp4 (3.3 MB)
            • soft_softmax
            • not_so_soft.ipynb (53.6 KB)
            07 Prelearing & UVR by Context Prediction (Theory)
            • 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)
            08 JigSaw
            • 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)
            09 Non-Parametric Instance Level Discrimination(NPILD) (hands on)
            • 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)
            10 PEARL
            • 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)

Description

Pytorch: Advanced Deep Learning (Computer Vision, DataAug)

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 1.36 GB | Duration: 3h 32m
Deep Learning : Deep Unsupervised Visual Representation learning (DUVRL) and SOTA Data Augmentation in Pytorch & Numpy
What you'll learn
Deep Unsupervised Visual Representation Learning Techniques
Deep Learning in Unsupervised manner
Computer Vision
Deep Learning for Computer Vision
Learn how companies like Google, Facebook build production level models
Basics of Pytorch & Numpy
Awesome SOTA Data Augmentation techniques in pytorch
Various properties of Softmax and CrossEntropy in Numpy & Pytorch
SimCLR (Simple Contrastive Learning)
Supervised contrastive learning
Non Parametric Instance Level Learning
Image Search and Cluster Search
Faiss Search
noise contrastive estimator
contrastive learning
SOTA papers like RandAug, JigSaw, PEARL, SimCLR, SupCon and many more..
Similarity metric like Cosine euclidean in Pytorch

Description
Published in 2021: Alpha Release

Dedicated to my Late Father KPS

You can take this course risk-free and if you don't like it, you can get a refund anytime in the first 30 days!

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[ FreeCourseWeb ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)

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1.4 GB
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[ FreeCourseWeb ] Pytorch - Advanced Deep Learning (Computer Vision, DataAug)

Torrent hash: BE7D0CC5BF248ACCE6B80BAE91199B4A45FF465E