PPT-Recent Developments in Deep Learning

Author : yoshiko-marsland | Published Date : 2015-09-20

Quoc V Le Stanford University and Google Purely supervised Quoc V Le Almost abandoned between 20002006 Overfitting slow many local minima gradient vanishing

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Recent Developments in Deep Learning: Transcript

Quoc V Le Stanford University and Google Purely supervised Quoc V Le Almost abandoned between 20002006 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). Rev.Adv.Mater.Sci. 18(2008) 203-211 RECENT DEVELOPMENTS IN MECHANICAL ALLOYING 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 . 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. …. Ronaldo Mercado. Epics Collaboration Meeting . Oak Ridge. Sep 2016. Outline. History. IOC count. Pilatus detectors with PPU. EPICS v4 plugin. Area Detector for ADCs. Computer vision – loop centring - at MX . 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.) . 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, . Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. 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 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.) . Eli Gutin. MIT 15.S60. (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. Millar BC, Moore JE. Emerging Issues in Infective Endocarditis. Emerg Infect Dis. 2004;10(6):1110-1116. https://doi.org/10.3201/eid1006.030848.

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