PPT-Deep Learning for Sequential Data

Models and applications Outline Sequence Data Recurrent Neural Networks Variants Handling Long Term Dependencies Attention Mechanisms Properties of RNNs Applications of RNNs Handson LSTMsupported timeseries prediction. Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . 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). Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. CS 678 – Deep Learning. 1. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). BA 445 Lesson B.3 Sequential Quantity Competition. I love . going down to the elementary school, watching all the kids jump and shout, but they don’t know I’m using blanks. . ~ Jack . Handey. .. to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . Lecture 8. Hartmut Kaiser. hkaiser@cct.lsu.edu. http://www.cct.lsu.edu/˜. hkaiser. /spring_2015/csc1254.html. Programming Principle of the Day. Principle of least . astonishment (POLA/PLA). The . principle of least astonishment is usually referenced in regards to the user interface, but the same principle applies to written code. . Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . Iterative Contraction and . Merging. Bayesian Sequential . Partitioning. JND-BSP. 1. Manifold Learning. Bosh Shih. 2. O. utline. Introduction. Principal Component Analysis (PCA. ). Linear Discriminant Analysis (LDA. Algorithms and Application s Xuyu Wang, Auburn University Abstract: With the rapid growth of mass data , how to intelligently proc ess these big data and extract valuable information from hug Double - 1 Double - Blind Sequential Police Lineup Procedures: Toward an Integrated Laboratory & Field Practice Perspective Final Report Grant # 2004 - IJ - CX - 0044 March 31, 2007 Nancy K. Steblay Primer on Sequential Design Methods . and Design Choices. Ronan Fitzpatrick. Lead Statistician. nQuery. Webinar. Host. Agenda. Sequential Design Overview. Issues in Sequential Design. Group Sequential Design.