PPT-Neural Networks and Deep Learning

Eli Gutin MIT 15S60 adapted from 2016 course by Iain Dunning Goals today Go over basics of neural nets Introduce TensorFlow Introduce Deep Learning Look at key applications Practice coding in Python. Information Processing & Artificial Intelligence. New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 4, 2013 (Day 3). Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Weifeng Li, . Victor Benjamin, Xiao . Liu, and . Hsinchun . Chen. University of Arizona. 1. Acknowledgements. Many of the pictures, results, and other materials are taken from:. Aarti. Singh, Carnegie Mellon University. Secada combs | bus-550. AI Superpowers: china, silicon valley, and the new world order. Kai Fu Lee. Author of AI Superpowers. Currently Chairman and CEO of . Sinovation. Ventures and President of . Sinovation. Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . Short-Term . Memory. Recurrent . Neural Networks. Meysam. . Golmohammadi. meysam@temple.edu. Neural Engineering Data Consortium. College . of Engineering . Temple University . February . 2016. Introduction. New-Generation Models & Methodology for Advancing . Speech Technology . and Information Processing. Li Deng . Microsoft Research, Redmond, . USA. CCF, . Beijing. , July . 8. , 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . Outline. What is Deep Learning. Tensors: Data Structures for Deep Learning. Multilayer Perceptron. Activation Functions for Deep Learning. Model Training in Deep Learning. Regularization for Deep Learning.