PPT-A General Distributed Deep Learning Platform

Author : pamella-moone | Published Date : 2017-04-14

SINGA Team September 4 2015 VLDB BOSS Apache SINGA Outline Part one Background SINGA Overview Programming Model System Architecture Experimental Study Research Challenges

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A General Distributed Deep Learning Platform: Transcript


SINGA Team September 4 2015 VLDB BOSS Apache SINGA Outline Part one Background SINGA Overview Programming Model System Architecture Experimental Study Research Challenges Conclusion Part two. 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 . Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . 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 End of the Joan of Arc . Teacher Recruitment Strategy. BEST-NC Innovation Lab. September 28, 2016. Cary, NC. Southern Regional Education Board. Andy Baxter, Vice President for Educator Effectiveness. 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. 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. Lessons from Adopting an Adaptive Learning Platform Speakers Jeremy Anderson Deputy Chief of Academic Technology Heather Bushey Director of SOUL & FIPSE Project Manager Criss Guy Online Course Builder 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.) . Assistant Professor. Computer Science and Engineering Department. Indian Institute of Technology Kharagpur. http://cse.iitkgp.ac.in/~adas/. Biological Neural Network. Image courtesy: F. . A. . Makinde. 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.

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