PPT-CNN 10 April 30 – May 3, 2018

Author : karlyn-bohler | Published Date : 2018-12-23

Agenda April 30 2018 Student News Note Taking Study Guide Communism Spreads in East Asia Student News April 30 2018 Worldchanging Promises a t South and North

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CNN 10 April 30 – May 3, 2018: Transcript

Agenda April 30 2018 Student News Note Taking Study Guide Communism Spreads in East Asia Student News April 30 2018 Worldchanging Promises a t South and North Korea Summit Curbing Violence in New York. If you dont find the answers at each stop be sure to ask your tour guide INTRODUCTION THEATER Lear n about the evolution of a news story from interviews in the field to satellites far beyond Earths atmosphere to newsrooms and then to televisions and These are the words nine young North Korean defectors had waited years to hear having traveled thousands of miles Unfortunately it was a lie The tragic story of this group of youngsters aged between 15 and 23 takes us back a few years when one by on V-JSAT2-May-15SUN3-May-15MON4-May-15V-BTUE5-May-15V-L/BV-JWED6-May-15THU7-May-15FRI8-May-15V-L/BV-JSAT9-May-15SUN10-May-15MON11-May-15V-BTUE12-May-15V-L/BV-J13-May-15THU14-May-15FRI15-May-15V-L/BV-JSA By Zhang . Liliang. Main idea: good features are no enough. VOC07: mAP:35.1. % -> 58.5%. Overview. (1) the model of R-CNN. (2) the result of R-CNN. (3) some discussions. Visualizing learned feature in CNN. WHO CONTRIBUTED IMMENSELY TO THE DEVELOPMENT OF INTERNATIONAL BROADCAST MEDIA. . . Wolf Blitzer. . Dan Rather. . Anderson Cooper. . Howard . Cosell. . . Timothy . Russert. .. . Christiane . Amanpou. Moitreya Chatterjee, . Yunan. . Luo. Image Source: Google. Outline – This Section. Why do we need Similarity Measures. Metric Learning as a measure of Similarity. Notion of a metric. Unsupervised Metric Learning. Carl . Doersch. Joint work with Alexei A. . Efros. . & . Abhinav. Gupta. ImageNet. + Deep Learning. Beagle. - Image Retrieval. - Detection (RCNN). - Segmentation (FCN). - Depth Estimation. - …. Yunchao. Wei, Wei Xia, . Junshi. Huang, . Bingbing. Ni, Jian Dong, Yao Zhao, Senior Member, IEEE . Shuicheng. Yan, Senior Member, IEEE. 2014. . arXiv. IEEE. . Short Papers. . HCPIssue. Date: Sept. 1 2016. Deformable Part Models with CNN Features. Pierre-André . Savalle. , . Stavros . Tsogkas. , George Papandreou, Iasonas Kokkinos. From HOG to CNN features. Detection . performance of C-DPM. Method. . CNN. KH Wong. CNN. V7b. 1. Introduction. Very Popular: . Toolboxes: . tensorflow. , . cuda-convnet. and . caffe. (user friendlier). A high performance Classifier (multi-class). Successful in object recognition, handwritten optical character OCR recognition, image noise removal etc.. Moitreya Chatterjee, . Yunan. . Luo. Image Source: Google. Outline – This Section. Why do we need Similarity Measures. Metric Learning as a measure of Similarity. Notion of a metric. Unsupervised Metric Learning. By Blake Ellis and Melanie Hicken, Senior Writers . Email us at watchdog@cnn.com. watchdog@cnn.com. Munif. CNN. The (CNN. ) . consists of: . . Convolutional layers. Subsampling Layers. Fully . connected . layers. Has achieved state-of-the-art result for the recognition of handwritten digits. Neural . Deep Learning Architectures. feed-forward . networks. auto-encoders (output want to recover input image, middle layer smaller - use results of middle layer for compression. ). recurrent neural networks (RNNs) (backward feeding at run time as part of input into middle .

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