PDF-A culturethat promotes learning The school is driven by a deep belief

6 The school ethos is built around high expectations and a commitment to excellence There is an expectation that every student will learn and achieve positive outcomes Classrooms are calm but busy a. (Belief:) It is going to rain. So, (Belief:) The streets will get wet. When I say that this process an end, and that we can reach more specific conclusions about which actions we have an obligati 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. 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). Thalamocortical. Circuits. Randall C. O’Reilly. University of Colorado Boulder. eCortex. , . Inc. Thalamocortical. Mysteries. What is function of the alpha rhythm (10Hz)?. Why does higher-order thalamus have a few strong driver inputs, and far more weaker ones?. 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. Carey . Nachenberg. Deep Learning for Dummies (Like me) – Carey . Nachenberg. (Like me). The Goal of this Talk?. Deep Learning for Dummies (Like me) – Carey . Nachenberg. 2. To provide you with . 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. …. 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, . 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 John-William . Sidhom. , MD/PhD Candidate ’21. Department of Biomedical Engineering. Johns Hopkins University School of Medicine. Sidney Kimmel Comprehensive Cancer Center. Johns Hopkins University. 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.) . Usman Roshan. NJIT. Derivative free optimization. Pros:. Can handle any activation function (for example sign). Free from vanishing and exploding gradient problems. Cons:. May take longer than gradient search.