PDF-Deep Learning

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. to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. 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?. Recognition. Author : . Kaiming. He, . Xiangyu. Zhang, . Shaoqing. Ren, and Jian Sun. (accepted to CVPR 2016). Presenter : . Hyeongseok. Son. The deeper, the better. The deeper network can cover more complex problems. 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 . 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. 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”. 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. 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. 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.