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[FreeCoursesOnline.Me] Coursera - Deep Learning in Computer Vision

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Description
[COURSERA] DEEP LEARNING IN COMPUTER VISION [FCO]

About this course: Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.

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File list
  • [FreeCoursesOnline.Me] Coursera - Deep Learning in Computer Vision
  • 001.Introduction and digital images/001. Short introduction to computer vision.mp4 15.3 MB
  • 001.Introduction and digital images/001. Short introduction to computer vision.srt 7.1 KB
  • 001.Introduction and digital images/002. Digital images.mp4 12.2 MB
  • 001.Introduction and digital images/002. Digital images.srt 5.1 KB
  • 001.Introduction and digital images/003. Structure of human eye and vision.mp4 22.3 MB
  • 001.Introduction and digital images/003. Structure of human eye and vision.srt 8.4 KB
  • 001.Introduction and digital images/004. Color models.mp4 57.7 MB
  • 001.Introduction and digital images/004. Color models.srt 21.5 KB
  • 002.Basic image processing/005. Image processing goals and tasks.mp4 10.8 MB
  • 002.Basic image processing/005. Image processing goals and tasks.srt 3.6 KB
  • 002.Basic image processing/006. Contrast and brightness correction.mp4 19.7 MB
  • 002.Basic image processing/006. Contrast and brightness correction.srt 7 KB
  • 002.Basic image processing/007. Image convolution.mp4 26 MB
  • 002.Basic image processing/007. Image convolution.srt 9.6 KB
  • 002.Basic image processing/008. Edge detection.mp4 31.9 MB
  • 002.Basic image processing/008. Edge detection.srt 11.7 KB
  • 003.Image classification/009. Recap Image classification.mp4 32.4 MB
  • 003.Image classification/009. Recap Image classification.srt 10.8 KB
  • 003.Image classification/010. AlexNet, VGG and Inception architectures.mp4 43.8 MB
  • 003.Image classification/010. AlexNet, VGG and Inception architectures.srt 14.2 KB
  • 003.Image classification/011. ResNet and beyond.mp4 43.2 MB
  • 003.Image classification/011. ResNet and beyond.srt 12.3 KB
  • 003.Image classification/012. Fine-grained image recognition.mp4 25.4 MB
  • 003.Image classification/012. Fine-grained image recognition.srt 7.5 KB
  • 003.Image classification/013. Detection and classification of facial attributes.mp4 24.1 MB
  • 003.Image classification/013. Detection and classification of facial attributes.srt 8.1 KB
  • 004.Content-based image retrieval/014. Content-based image retrieval.mp4 31.6 MB
  • 004.Content-based image retrieval/014. Content-based image retrieval.srt 9.3 KB
  • 004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.mp4 35.6 MB
  • 004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.srt 11 KB
  • 004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.mp4 37.3 MB
  • 004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.srt 11.7 KB
  • 004.Content-based image retrieval/017. Face verification.mp4 25.2 MB
  • 004.Content-based image retrieval/017. Face verification.srt 7.9 KB
  • 004.Content-based image retrieval/018. The re-identification problem in computer vision.mp4 21.1 MB
  • 004.Content-based image retrieval/018. The re-identification problem in computer vision.srt 6.8 KB
  • 005.Keypoints regression/019. Facial keypoints regression.mp4 25.6 MB
  • 005.Keypoints regression/019. Facial keypoints regression.srt 7.5 KB
  • 005.Keypoints regression/020. CNN for keypoints regression.mp4 23.2 MB
  • 005.Keypoints regression/020. CNN for keypoints regression.srt 7.2 KB
  • 006.Sliding window detectors/021. Object detection problem.mp4 22.4 MB
  • 006.Sliding window detectors/021. Object detection problem.srt 9.8 KB
  • 006.Sliding window detectors/022. Sliding windows.mp4 11.8 MB
  • 006.Sliding window detectors/022. Sliding windows.srt 4.7 KB
  • 006.Sliding window detectors/023. HOG-based detector.mp4 9.1 MB
  • 006.Sliding window detectors/023. HOG-based detector.srt 3.4 KB
  • 006.Sliding window detectors/024. Detector training.mp4 11.7 MB
  • 006.Sliding window detectors/024. Detector training.srt 4.4 KB
  • 006.Sliding window detectors/025. Viola-Jones face detector.mp4 19.4 MB
  • 006.Sliding window detectors/025. Viola-Jones face detector.srt 8 KB
  • 006.Sliding window detectors/026. Attentional cascades and neural networks.mp4 12.2 MB
  • 006.Sliding window detectors/026. Attentional cascades and neural networks.srt 4.8 KB
  • 007.Modern detector architectures/027. Region-based convolutional neural network.mp4 10.7 MB
  • 007.Modern detector architectures/027. Region-based convolutional neural network.srt 4.3 KB
  • 007.Modern detector architectures/028. From R-CNN to Fast R-CNN.mp4 17.8 MB
  • 007.Modern detector architectures/028. From R-CNN to Fast R-CNN.srt 6.9 KB
  • 007.Modern detector architectures/029. Faster R-CNN.mp4 15.8 MB
  • 007.Modern detector architectures/029. Faster R-CNN.srt 5.7 KB
  • 007.Modern detector architectures/030. Region-based fully-convolutional network.mp4 8.5 MB
  • 007.Modern detector architectures/030. Region-based fully-convolutional network.srt 3.1 KB
  • 007.Modern detector architectures/031. Single shot detectors.mp4 14.5 MB
  • 007.Modern detector architectures/031. Single shot detectors.srt 2.5 KB
  • 007.Modern detector architectures/032. Speed vs. accuracy tradeoff.mp4 7.1 MB
  • 007.Modern detector architectures/032. Speed vs. accuracy tradeoff.srt 2.5 KB
  • 007.Modern detector architectures/033. Fun with pedestrian detectors.mp4 5.8 MB
  • 007.Modern detector architectures/033. Fun with pedestrian detectors.srt 1.6 KB
  • 008.Object tracking/034. Introduction to video analysis.mp4 12.7 MB
  • 008.Object tracking/034. Introduction to video analysis.srt 5.2 KB
  • 008.Object tracking/035. Optical flow.mp4 17.3 MB
  • 008.Object tracking/035. Optical flow.srt 7.8 KB
  • 008.Object tracking/036. Deep learning in optical flow estimation.mp4 19 MB
  • 008.Object tracking/036. Deep learning in optical flow estimation.srt 8.6 KB
  • 008.Object tracking/037. Visual object tracking.mp4 18.7 MB
  • 008.Object tracking/037. Visual object tracking.srt 8.1 KB
  • 008.Object tracking/038. Examples of visual object tracking methods.mp4 42.9 MB
  • 008.Object tracking/038. Examples of visual object tracking methods.srt 20.8 KB
  • 008.Object tracking/039. Multiple object tracking.mp4 18.2 MB
  • 008.Object tracking/039. Multiple object tracking.srt 8.1 KB
  • 008.Object tracking/040. Examples of multiple object tracking methods.mp4 26.3 MB
  • 008.Object tracking/040. Examples of multiple object tracking methods.srt 12 KB
  • 009.Action recognition/041. Introduction to action recognition.mp4 21.9 MB
  • 009.Action recognition/041. Introduction to action recognition.srt 9.3 KB
  • 009.Action recognition/042. Action classification.mp4 26.6 MB
  • 009.Action recognition/042. Action classification.srt 11.9 KB
  • 009.Action recognition/043. Action classification with convolutional neural networks.mp4 18.8 MB
  • 009.Action recognition/043. Action classification with convolutional neural networks.srt 8.2 KB
  • 009.Action recognition/044. Action localization.mp4 22.4 MB
  • 009.Action recognition/044. Action localization.srt 10.1 KB
  • 010.Image segmentation/045. Image segmentation.mp4 16 MB
  • 010.Image segmentation/045. Image segmentation.srt 4.8 KB
  • 010.Image segmentation/046. Oversegmentation.mp4 17.8 MB
  • 010.Image segmentation/046. Oversegmentation.srt 5.4 KB
  • 010.Image segmentation/047. Deep learning models for image segmentation.mp4 32.7 MB
  • 010.Image segmentation/047. Deep learning models for image segmentation.srt 10.1 KB
  • 010.Image segmentation/048. Human pose estimation as image segmentation.mp4 33.4 MB
  • 010.Image segmentation/048. Human pose estimation as image segmentation.srt 10.9 KB
  • 011.Style transfer and image generation/049. Style transfer.mp4 22.7 MB
  • 011.Style transfer and image generation/049. Style transfer.srt 6.6 KB
  • 011.Style transfer and image generation/050. Generative adversarial networks.mp4 29.5 MB

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