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. Brains and games. Introduction. Spiking Neural Networks are a variation of traditional NNs that attempt to increase the realism of the simulations done. They more closely resemble the way brains actually operate. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. 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. 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. 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. 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. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. 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. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. 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.