PPT-6.S093 Visual Recognition through Machine Learning Competit

Image by kirkhdeviantartcom Aditya Khosla and Joseph Lim Todays class Part 1 Introduction to deep learning What is deep learning Why deep learning Some common deep learning algorithms. 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. e World Bank Poverty Reduction and Economic Management NetworkInternational Trade DepartmentApril 2013 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Discl Jimmy Lin and Alek . Kolcz. Twitter, Inc.. Presented by: Yishuang Geng and Kexin Liu. 2. Outline. •Is twitter big data? . •How . can machine learning help twitter?. •Existing challenges?. •Existing literature of large-scale learning. Recognition tasks. Machine learning approach: training, testing, generalization. Example classifiers. Nearest neighbor. Linear classifiers. Image features. Spatial support:. Pixel or local patch. Segmentation region. http://hunch.net/~mltf. John Langford. Microsoft Research. Machine Learning in the present. Get a large amount of labeled data . . where . . Learn a predictor . Use the predictor.. The Foundation: Samples + Representation + Optimization. 1. Speech Recognition and HMM Learning. Overview of speech recognition approaches. Standard Bayesian Model. Features. Acoustic Model Approaches. Language Model. Decoder. Issues. Hidden Markov Models. David Kauchak. CS 451 – Fall 2013. Why are you here?. What is Machine Learning?. Why are you taking this course?. What topics would you like to see covered?. Machine Learning is…. Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.. COS 518: Advanced Computer Systems. Lecture . 13. Daniel Suo. Outline. 2. What is machine learning?. Why is machine learning hard in parallel / distributed systems?. A brief history of what people have done. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition. The inability to recognise familiar objects presented visually is known as visual . agnosia. . There are two main types:. Apperceptive. . agnosia. – a failure to recognise due to impaired visual perception. This implies a physiological problem in the visual system.. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition. Deep Learning for Expression Recognition in Image Sequences Daniel Natanael García Zapata Tutors: Dr. Sergio Escalera Dr. Gholamreza Anbarjafari April 27 2018 Introduction and Goals Introduction Dennis Hamester et al., “Face ExpressionRecognition with a 2-Channel ConvolutionalNeural Network”, International Joint Conference on Neural Networks (IJCNN), 2015. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. (CS725). Autumn 2011. Instructor: . Prof. . Ganesh. . Ramakrishnan. TAs: . Ajay Nagesh, Amrita . Saha. , . Kedharnath. . Narahari. The grand goal. From the movie . 2001: A Space Odyssey. (1968). Outline.