[CourseClub.NET] Coursera - Deep Learning in Computer Vision

Creation Time	Sept. 9, 2023, 1:52 p.m.
Last Access Time	July 22, 2026, 12:20 a.m.
File Size	1.1 GB
Keywords	CourseClub Coursera - Deep Computer Learning in NET Vision
Total Requests	441
Total Files	104
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001.Introduction and digital images/004. Color models.mp4 57.7 MB
003.Image classification/010. AlexNet, VGG and Inception architectures.mp4 43.8 MB
003.Image classification/011. ResNet and beyond.mp4 43.2 MB
008.Object tracking/038. Examples of visual object tracking methods.mp4 42.9 MB
004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.mp4 37.3 MB
004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.mp4 35.6 MB
010.Image segmentation/048. Human pose estimation as image segmentation.mp4 33.4 MB
010.Image segmentation/047. Deep learning models for image segmentation.mp4 32.7 MB
003.Image classification/009. Recap Image classification.mp4 32.4 MB
002.Basic image processing/008. Edge detection.mp4 31.9 MB
004.Content-based image retrieval/014. Content-based image retrieval.mp4 31.6 MB
011.Style transfer and image generation/050. Generative adversarial networks.mp4 29.5 MB
009.Action recognition/042. Action classification.mp4 26.6 MB
008.Object tracking/040. Examples of multiple object tracking methods.mp4 26.3 MB
002.Basic image processing/007. Image convolution.mp4 26.0 MB
005.Keypoints regression/019. Facial keypoints regression.mp4 25.6 MB
003.Image classification/012. Fine-grained image recognition.mp4 25.4 MB
004.Content-based image retrieval/017. Face verification.mp4 25.2 MB
003.Image classification/013. Detection and classification of facial attributes.mp4 24.1 MB
005.Keypoints regression/020. CNN for keypoints regression.mp4 23.2 MB
011.Style transfer and image generation/051. Image transformation with neural networks.mp4 22.7 MB
011.Style transfer and image generation/049. Style transfer.mp4 22.7 MB
006.Sliding window detectors/021. Object detection problem.mp4 22.4 MB
009.Action recognition/044. Action localization.mp4 22.4 MB
001.Introduction and digital images/003. Structure of human eye and vision.mp4 22.3 MB
009.Action recognition/041. Introduction to action recognition.mp4 21.9 MB
004.Content-based image retrieval/018. The re-identification problem in computer vision.mp4 21.1 MB
002.Basic image processing/006. Contrast and brightness correction.mp4 19.7 MB
006.Sliding window detectors/025. Viola-Jones face detector.mp4 19.4 MB
008.Object tracking/036. Deep learning in optical flow estimation.mp4 19.0 MB
009.Action recognition/043. Action classification with convolutional neural networks.mp4 18.8 MB
008.Object tracking/037. Visual object tracking.mp4 18.7 MB
008.Object tracking/039. Multiple object tracking.mp4 18.2 MB
010.Image segmentation/046. Oversegmentation.mp4 17.8 MB
007.Modern detector architectures/028. From R-CNN to Fast R-CNN.mp4 17.8 MB
008.Object tracking/035. Optical flow.mp4 17.3 MB
010.Image segmentation/045. Image segmentation.mp4 16.0 MB
007.Modern detector architectures/029. Faster R-CNN.mp4 15.8 MB
001.Introduction and digital images/001. Short introduction to computer vision.mp4 15.3 MB
007.Modern detector architectures/031. Single shot detectors.mp4 14.5 MB
008.Object tracking/034. Introduction to video analysis.mp4 12.7 MB
006.Sliding window detectors/026. Attentional cascades and neural networks.mp4 12.2 MB
001.Introduction and digital images/002. Digital images.mp4 12.2 MB
006.Sliding window detectors/022. Sliding windows.mp4 11.8 MB
006.Sliding window detectors/024. Detector training.mp4 11.7 MB
002.Basic image processing/005. Image processing goals and tasks.mp4 10.8 MB
007.Modern detector architectures/027. Region-based convolutional neural network.mp4 10.7 MB
006.Sliding window detectors/023. HOG-based detector.mp4 9.1 MB
007.Modern detector architectures/030. Region-based fully-convolutional network.mp4 8.5 MB
007.Modern detector architectures/032. Speed vs. accuracy tradeoff.mp4 7.1 MB
007.Modern detector architectures/033. Fun with pedestrian detectors.mp4 5.8 MB
001.Introduction and digital images/004. Color models.srt 21.5 KB
008.Object tracking/038. Examples of visual object tracking methods.srt 20.8 KB
003.Image classification/010. AlexNet, VGG and Inception architectures.srt 14.2 KB
003.Image classification/011. ResNet and beyond.srt 12.3 KB
008.Object tracking/040. Examples of multiple object tracking methods.srt 12.0 KB
009.Action recognition/042. Action classification.srt 11.9 KB
002.Basic image processing/008. Edge detection.srt 11.7 KB
004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.srt 11.7 KB
004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.srt 11.0 KB
010.Image segmentation/048. Human pose estimation as image segmentation.srt 10.9 KB
003.Image classification/009. Recap Image classification.srt 10.8 KB
010.Image segmentation/047. Deep learning models for image segmentation.srt 10.1 KB
009.Action recognition/044. Action localization.srt 10.1 KB
011.Style transfer and image generation/050. Generative adversarial networks.srt 9.9 KB
006.Sliding window detectors/021. Object detection problem.srt 9.8 KB
002.Basic image processing/007. Image convolution.srt 9.6 KB
009.Action recognition/041. Introduction to action recognition.srt 9.3 KB
004.Content-based image retrieval/014. Content-based image retrieval.srt 9.3 KB
008.Object tracking/036. Deep learning in optical flow estimation.srt 8.6 KB
001.Introduction and digital images/003. Structure of human eye and vision.srt 8.4 KB
009.Action recognition/043. Action classification with convolutional neural networks.srt 8.2 KB
008.Object tracking/039. Multiple object tracking.srt 8.1 KB
003.Image classification/013. Detection and classification of facial attributes.srt 8.1 KB
008.Object tracking/037. Visual object tracking.srt 8.1 KB
006.Sliding window detectors/025. Viola-Jones face detector.srt 8.0 KB
004.Content-based image retrieval/017. Face verification.srt 7.9 KB
008.Object tracking/035. Optical flow.srt 7.8 KB
005.Keypoints regression/019. Facial keypoints regression.srt 7.5 KB
003.Image classification/012. Fine-grained image recognition.srt 7.5 KB
005.Keypoints regression/020. CNN for keypoints regression.srt 7.2 KB
001.Introduction and digital images/001. Short introduction to computer vision.srt 7.1 KB
002.Basic image processing/006. Contrast and brightness correction.srt 7.0 KB
007.Modern detector architectures/028. From R-CNN to Fast R-CNN.srt 6.9 KB
004.Content-based image retrieval/018. The re-identification problem in computer vision.srt 6.8 KB
011.Style transfer and image generation/049. Style transfer.srt 6.6 KB
007.Modern detector architectures/029. Faster R-CNN.srt 5.7 KB
011.Style transfer and image generation/051. Image transformation with neural networks.srt 5.6 KB
010.Image segmentation/046. Oversegmentation.srt 5.4 KB
008.Object tracking/034. Introduction to video analysis.srt 5.2 KB
001.Introduction and digital images/002. Digital images.srt 5.1 KB
010.Image segmentation/045. Image segmentation.srt 4.8 KB
006.Sliding window detectors/026. Attentional cascades and neural networks.srt 4.8 KB
006.Sliding window detectors/022. Sliding windows.srt 4.7 KB
006.Sliding window detectors/024. Detector training.srt 4.4 KB
007.Modern detector architectures/027. Region-based convolutional neural network.srt 4.3 KB
002.Basic image processing/005. Image processing goals and tasks.srt 3.6 KB
006.Sliding window detectors/023. HOG-based detector.srt 3.4 KB
007.Modern detector architectures/030. Region-based fully-convolutional network.srt 3.1 KB
007.Modern detector architectures/031. Single shot detectors.srt 2.5 KB
007.Modern detector architectures/032. Speed vs. accuracy tradeoff.srt 2.5 KB
007.Modern detector architectures/033. Fun with pedestrian detectors.srt 1.6 KB
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[CourseClub.NET] Coursera - Deep Learning in Computer Vision

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