PPT-Food Dish Classification using Convolutional Neural Networks

Edgar de la Torre Bazan Professor Dr Dong Chul Kim Department of of Engineering and Computer Science The University of Texas Rio Grande Valley   Motivation The main motivation for this project is to help individuals automatically identify the dishes they have documented on their social media This is a complex task due to many variations of the same food dishes in the world We believe that I applying machine learning technology will simplify this task and help individuals improve their health by monitoring their food intake. 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 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. Kuan-Chuan. Peng. Tsuhan. Chen. 1. Introduction. Breakthrough progress in object classification.. 2. O. . Russakovsky. . et al. . ImageNet. . large scale visual recognition challenge. .. . arXiv:1409.0575, 2014.. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. 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. 2017-03-24. 조수현. Contents. Extrinsic task. Softmax. classification and regularization. Window classification. Neural networks. Extrinsic task. Extrinsic task:. Using the resulting word vectors for some other extrinsic task. Convolutions. Reduce parameters. Capture shift-invariance: location of patch in image should not matter. Subsampling. Allows greater invariance to deformations. Allows the capture of large patterns with small filters. Generally a DAG, directed acyclic graph. VisGraph, HKUST. LeNet. AlexNet. ZF Net. GoogLeNet. VGGNet. ResNet. Learned convolutional filters: Stage 1. Visualizing and understanding convolutional neural networks.. 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 . Taras. . Mitran. Jeff Waller. HR Compensation Workflow. Scenario: ABC Corp wants to hire a statistician.. What the market rate for this job, at the 50. th. percentile? 60%ile?. Issue: Almost every company’s job title and description for roughly the same “job” is different than other companies.. . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments.. 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.