PPT-Hands-On Deep Learning

with MatConvNet wwwcvcuabesgrosindexphphandsondeeplearningwithmatconvnet 15 th January of 2015 Training antelope ballet boat Empirical Risk Training Stochastic gradient descent. 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. 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). 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 . 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 . Rajdeep. . Dasgupta. CIDER Community Workshop, CA. May 08, 2016. Volcanic degassing. hazards. long-term climate. Bio-essential elements. Origin of life. Mantle melting. Chemical differentiation. Properties of asthenosphere. 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”. 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. Presenter : Jingyun Ning. “CVPR 2016 Best Paper Award”. Introduction. Deep Residual Networks (ResNets). A simple and clean framework of training “very” deep nets. State-of-the-art performance for. 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. 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.) . 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. Patient Cohort Retrieval . Sanda. . Harabagiu. , . PhD. , Travis Goodwin, Ramon Maldonado, Stuart Taylor . The . Human Language Technology Research Institute. University of Texas at Dallas. Human Language Technology.