PPT-Gradient-based Learning Applied to Document Recognition

Yann LeCun Leon Bottou Yoshua Bengio and Patrick Haffner 1998 1 Ofir Liba Michael Kotlyar Deep learning seminar 20167 Outline Introduction Convolution neural network LeNet5. Rights Reserved Page | 85 Volume 2, Issue 5 , May 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available Focus on Learning, . Part 2. Mark Hoddenbagh. 2012 June 05. St. Lawrence College. Through . active participation in the Focus on Learning Program, participants will have demonstrated their ability . to f. S . Amari. 11.03.18.(Fri). Computational Modeling of Intelligence. Summarized by . Joon. . Shik. Kim. Abstract. The ordinary gradient of a function does not represent its steepest direction, but the natural gradient does.. :. A Literature Survey. By:. W. Zhao, R. Chellappa, P.J. Phillips,. and A. Rosenfeld. Presented By:. Diego Velasquez. Contents . Introduction. Why do we need face recognition?. Biometrics. Face Recognition by Humans. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. Machine Learning. Large scale machine learning. Machine learning and data. Classify between confusable words.. E.g., {to, two, too}, {then, than}.. For breakfast I ate _____ eggs.. “It’s not who has the best algorithm that wins. . 1. Speech Recognition and HMM Learning. Overview of speech recognition approaches. Standard Bayesian Model. Features. Acoustic Model Approaches. Language Model. Decoder. Issues. Hidden Markov Models. G.Anuradha. Review of previous lecture-. Steepest Descent. Choose the next step so that the function decreases:. For small changes in . x. we can approximate . F. (. x. ):. where. If we want the function to decrease:. 1. Neural. . Function. Brain function (thought) occurs . as the result . of . the. . firing . of. . neurons. Neurons . connect . to each . other through . synapses. , . which . propagate . action potential . Applications. Lectures 12-13: . Regularization and Optimization. Zhu Han. University of Houston. Thanks . Xusheng. Du and Kevin Tsai For Slide Preparation. 1. Part 1 Regularization Outline. Parameter Norm Penalties. Goals of Weeks 5-6. What is machine learning (ML) and when is it useful?. Intro to major techniques and applications. Give examples. How can CUDA help?. Departure from usual pattern: we will give the application first, and the CUDA later. Nima Aghaee, Zebo Peng, and Petru Eles. Embedded Systems Laboratory (ESLAB). Linkoping University. 12th Swedish System-on-Chip Conference – May 2013. Outline. Introduction. Early life failures. Temperature gradient effects. Ryota Tomioka (. ryoto@microsoft.com. ). MSR Summer School. 2 July 2018. Azure . iPython. Notebook. https://notebooks.azure.com/ryotat/libraries/DLTutorial. Agenda. This lecture covers. Introduction to machine learning.