Coursera – Deep Learning in Computer Vision | | MP4,URL | 1.12 GiB
459 kb/s 1280×720 | AAC 128 kb/s 2 CH eng
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 4.0K [FreeCoursesOnline.Me].url 4.0K [FreeTutorials.Us].url 1.2G total
File: 001. Short introduction to computer vision.mp4 Size: 16076247 bytes (15.33 MiB), duration: 00:04:40, avg.bitrate: 459 kb/s Audio: aac, 44100 Hz, stereo (eng) Video: h264, yuv420p, 1280x720, 25.00 fps(r) (eng)
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