PPT-Convolutional Neural Network

20151002 陳柏任 Outline Neural Networks Convolutional Neural Networks Some famous CNN structure Applications Toolkit Conclusion Reference 2 Outline Neural Networks Convolutional Neural Networks. ABSTRACT From the desire to update the maximum road speed data for navigation devices a speed sign recognition and detection system is proposed This system should prevent accidental speeding at roads where the map data is incorrect for example due t RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. using Convolutional Neural Network and Simple Logistic Classifier. Hurieh. . Khalajzadeh. Mohammad . Mansouri. Mohammad . Teshnehlab. Table of Contents. Convolutional Neural . Networks. Proposed CNN structure for face recognition. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. 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 . Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . Daniel Boonzaaier. Supervisor – Adiel Ismail. April 2017. Content. Project Overview. Checkers – the board game. Background on Neural Networks. Neural Network applied to Checkers. Requirements. Project Plan. Prabhas. . Chongstitvatana. Faculty of Engineering. Chulalongkorn. university. More Information. Search “Prabhas Chongstitvatana”. Go to me homepage. Perceptron. Rosenblatt, 1950. Multi-layer perceptron. An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.