PPT-Deep Learning: Speech & Information Processing

NewGeneration Models amp 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 . 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 . 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). Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Presenter: . Yanming. . Guo. Adviser: Dr. Michael S. Lew. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep Learning. Why better?. CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. Aaron Schumacher. Data Science DC. 2017-11-14. Aaron Schumacher. planspace.org has these slides. Plan. applications. : . what. t. heory. applications. : . how. onward. a. pplications: what. Backgammon. MATLAB Speech Processing Code. MATLAB GUI Implementations. 1. Lecture_3_2013. Graphical User Interface. GUI Lite 2.6. 2. Waveform Strips Plot. 3. Basics. How do we rapidly and efficiently create a GUI for problems like the one shown above?. New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 2-5, 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . 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, . Assessment, Diagnosis, Treatment and Controversies. Defining Auditory Processing and APD. Auditory processing . may be described as the “efficiency and effectiveness by which the central nervous system (CNS) utilizes auditory information.” (ASHA 2005). University of Central Florida. July 20, 2012. Applications of Images and Signals in High Schools. Contributors. Dr. . . Veton. . Këpuska. , . Faculty Mentor, FIT. vkepuska@fit.edu. Jacob . Zurasky. 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 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. Julia Hirschberg. CS 6998. 1/17/23. 1. Welcome to the Course!. 1/17/23. 2. My Background. First PhD in History at . UMich. on 16. th. -century first settlers in Puebla de los Angeles, Mexico. Taught at Smith College.