Coursera – Deep Learning in Computer Vision

108M	001.Introduction and digital images
16M	001.Introduction and digital images/001. Short introduction to computer vision.mp4
8.0K	001.Introduction and digital images/001. Short introduction to computer vision.srt
13M	001.Introduction and digital images/002. Digital images.mp4
8.0K	001.Introduction and digital images/002. Digital images.srt
23M	001.Introduction and digital images/003. Structure of human eye and vision.mp4
12K	001.Introduction and digital images/003. Structure of human eye and vision.srt
58M	001.Introduction and digital images/004. Color models.mp4
24K	001.Introduction and digital images/004. Color models.srt
89M	002.Basic image processing
11M	002.Basic image processing/005. Image processing goals and tasks.mp4
4.0K	002.Basic image processing/005. Image processing goals and tasks.srt
20M	002.Basic image processing/006. Contrast and brightness correction.mp4
8.0K	002.Basic image processing/006. Contrast and brightness correction.srt
27M	002.Basic image processing/007. Image convolution.mp4
12K	002.Basic image processing/007. Image convolution.srt
32M	002.Basic image processing/008. Edge detection.mp4
12K	002.Basic image processing/008. Edge detection.srt
169M	003.Image classification
33M	003.Image classification/009. Recap  Image classification.mp4
12K	003.Image classification/009. Recap  Image classification.srt
44M	003.Image classification/010. AlexNet, VGG and Inception architectures.mp4
16K	003.Image classification/010. AlexNet, VGG and Inception architectures.srt
44M	003.Image classification/011. ResNet and beyond.mp4
16K	003.Image classification/011. ResNet and beyond.srt
26M	003.Image classification/012. Fine-grained image recognition.mp4
8.0K	003.Image classification/012. Fine-grained image recognition.srt
25M	003.Image classification/013. Detection and classification of facial attributes.mp4
12K	003.Image classification/013. Detection and classification of facial attributes.srt
151M	004.Content-based image retrieval
32M	004.Content-based image retrieval/014. Content-based image retrieval.mp4
12K	004.Content-based image retrieval/014. Content-based image retrieval.srt
36M	004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.mp4
12K	004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.srt
38M	004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.mp4
12K	004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.srt
26M	004.Content-based image retrieval/017. Face verification.mp4
8.0K	004.Content-based image retrieval/017. Face verification.srt
22M	004.Content-based image retrieval/018. The re-identification problem in computer vision.mp4
8.0K	004.Content-based image retrieval/018. The re-identification problem in computer vision.srt
49M	005.Keypoints  regression
26M	005.Keypoints  regression/019. Facial keypoints regression.mp4
8.0K	005.Keypoints  regression/019. Facial keypoints regression.srt
24M	005.Keypoints  regression/020. CNN for keypoints regression.mp4
8.0K	005.Keypoints  regression/020. CNN for keypoints regression.srt
87M	006.Sliding window detectors
23M	006.Sliding window detectors/021. Object detection problem.mp4
12K	006.Sliding window detectors/021. Object detection problem.srt
12M	006.Sliding window detectors/022. Sliding windows.mp4
8.0K	006.Sliding window detectors/022. Sliding windows.srt
9.2M	006.Sliding window detectors/023. HOG-based detector.mp4
4.0K	006.Sliding window detectors/023. HOG-based detector.srt
12M	006.Sliding window detectors/024. Detector training.mp4
8.0K	006.Sliding window detectors/024. Detector training.srt
20M	006.Sliding window detectors/025. Viola-Jones face detector.mp4
8.0K	006.Sliding window detectors/025. Viola-Jones face detector.srt
13M	006.Sliding window detectors/026. Attentional cascades and neural networks.mp4
8.0K	006.Sliding window detectors/026. Attentional cascades and neural networks.srt
81M	007.Modern detector architectures
11M	007.Modern detector architectures/027. Region-based convolutional neural network.mp4
8.0K	007.Modern detector architectures/027. Region-based convolutional neural network.srt
18M	007.Modern detector architectures/028. From R-CNN to Fast R-CNN.mp4
8.0K	007.Modern detector architectures/028. From R-CNN to Fast R-CNN.srt
16M	007.Modern detector architectures/029. Faster R-CNN.mp4
8.0K	007.Modern detector architectures/029. Faster R-CNN.srt
8.6M	007.Modern detector architectures/030. Region-based fully-convolutional network.mp4
4.0K	007.Modern detector architectures/030. Region-based fully-convolutional network.srt
15M	007.Modern detector architectures/031. Single shot detectors.mp4
4.0K	007.Modern detector architectures/031. Single shot detectors.srt
7.1M	007.Modern detector architectures/032. Speed vs. accuracy tradeoff.mp4
4.0K	007.Modern detector architectures/032. Speed vs. accuracy tradeoff.srt
5.9M	007.Modern detector architectures/033. Fun with pedestrian detectors.mp4
4.0K	007.Modern detector architectures/033. Fun with pedestrian detectors.srt
156M	008.Object tracking
13M	008.Object tracking/034. Introduction to video analysis.mp4
8.0K	008.Object tracking/034. Introduction to video analysis.srt
18M	008.Object tracking/035. Optical flow.mp4
8.0K	008.Object tracking/035. Optical flow.srt
19M	008.Object tracking/036. Deep learning in optical flow estimation.mp4
12K	008.Object tracking/036. Deep learning in optical flow estimation.srt
19M	008.Object tracking/037. Visual object tracking.mp4
12K	008.Object tracking/037. Visual object tracking.srt
43M	008.Object tracking/038. Examples of visual object tracking methods.mp4
24K	008.Object tracking/038. Examples of visual object tracking methods.srt
19M	008.Object tracking/039. Multiple object tracking.mp4
12K	008.Object tracking/039. Multiple object tracking.srt
27M	008.Object tracking/040. Examples of multiple object tracking methods.mp4
12K	008.Object tracking/040. Examples of multiple object tracking methods.srt
90M	009.Action recognition
22M	009.Action recognition/041. Introduction to action recognition.mp4
12K	009.Action recognition/041. Introduction to action recognition.srt
27M	009.Action recognition/042. Action classification.mp4
12K	009.Action recognition/042. Action classification.srt
19M	009.Action recognition/043. Action classification with convolutional neural networks.mp4
12K	009.Action recognition/043. Action classification with convolutional neural networks.srt
23M	009.Action recognition/044. Action localization.mp4
12K	009.Action recognition/044. Action localization.srt
101M	010.Image segmentation
17M	010.Image segmentation/045. Image segmentation.mp4
8.0K	010.Image segmentation/045. Image segmentation.srt
18M	010.Image segmentation/046. Oversegmentation.mp4
8.0K	010.Image segmentation/046. Oversegmentation.srt
33M	010.Image segmentation/047. Deep learning models for image segmentation.mp4
12K	010.Image segmentation/047. Deep learning models for image segmentation.srt
34M	010.Image segmentation/048. Human pose estimation as image segmentation.mp4
12K	010.Image segmentation/048. Human pose estimation as image segmentation.srt
75M	011.Style transfer and image generation
23M	011.Style transfer and image generation/049. Style transfer.mp4
8.0K	011.Style transfer and image generation/049. Style transfer.srt
30M	011.Style transfer and image generation/050. Generative adversarial networks.mp4
12K	011.Style transfer and image generation/050. Generative adversarial networks.srt
23M	011.Style transfer and image generation/051. Image transformation with neural networks.mp4
8.0K	011.Style transfer and image generation/051. Image transformation with neural networks.srt
4.0K	[FTU Forum].url
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1.2G	total