PPT-Part 2: An Analysis of Single-Layer Networks in Unsupervised Feature Learning

Author : faustina-dinatale | Published Date : 2018-10-30

Adam Coates Honglak Lee Andrew Y Ng 20170309 1 Introduction Feature learningrepresentation is a major topic when processing unlabeled highdimensional data For

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Part 2: An Analysis of Single-Layer Networks in Unsupervised Feature Learning: Transcript

Adam Coates Honglak Lee Andrew Y Ng 20170309 1 Introduction Feature learningrepresentation is a major topic when processing unlabeled highdimensional data For example how to cluster images by recognizing the objects inside. 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 . 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). . Image by kirkh.deviantart.com. Aditya. . Khosla. and Joseph Lim. Today’s class. Part 1: Introduction to deep learning. What is deep learning?. Why deep learning?. Some common deep learning algorithms. Matthew D. . Zeiler. Dilip. Krishnan, Graham W. Taylor. Rob Fergus. Dept. of Computer Science, Courant Institute, . New York University. Matt . Zeiler. Overview. Unsupervised learning of. mid and high-level image representations. 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. 1. Last Time. Perceptrons. Perceptron. Loss vs. Logistic Regression Loss. Training . Perceptrons. and Logistic Regression Models using Gradient Descent. 2. Today. Multilayer Neural Networks. CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. Lecture 1. . Email: gurdip@ksu.edu. http://www.cis.ksu.edu/~singh. Phone: (785) 532-7945. Fax: (785) 532-7353. Nichols 234C. Books. Computer Networks (not required). Andrew . Tanenbaum. Big . Data and Deep Learning. Big Data seminar. Presentation 10.14.15. Outline. Emotiv. demo. Data Acquisition. Cognitive models for emotions recognition. Big Data. Deep Learning. . Human Brain: the Big Data model. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Unsu. pervised . approaches . for . word sense disambiguation. Under the guidance of. Slides by. Arindam. . Chatterjee. &. Salil. Joshi. Prof. . Pushpak . Bhattacharyya. May 01, 2010. roadmap. Bird’s Eye View.. FROM BIG DATA. Richard Holaj. Humor GENERATING . introduction. very hard . problem. . deep. . semantic. . understanding. . cultural. . contextual. . clues. . solutions. . using. . labelling.

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