PPT-Deep Linear Networks Kaiqi

Jiang Feb 17 Model formulation             Recall the model of fullyconnected neural networks   When   Linear Networks In the following slides we only consider linear networks without bias. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. 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. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. Applications. Lectures 11-12: Deep Learning Basics. Zhu Han. University of Houston. Thanks for Dr. . Hien. Nguyen slides and help by . Xunshen. Du and Kevin Tsai. 1. outline. Motivation and overview. Linear classifiers on pixels are bad. Solution 1: Better feature vectors. Solution 2: Non-linear classifiers. A pipeline for recognition. Compute image gradients. Compute SIFT descriptors. Assign to k-means centers. Convolutions. Reduce parameters. Capture shift-invariance: location of patch in image should not matter. Subsampling. Allows greater invariance to deformations. Allows the capture of large patterns with small filters. . Jude Shavlik. Yuting. . Liu (TA). Deep Learning (DL). Deep Neural Networks arguably the most exciting current topic in all of CS. Huge industrial and academic impact. Great intellectual challenges. Secada combs | bus-550. AI Superpowers: china, silicon valley, and the new world order. Kai Fu Lee. Author of AI Superpowers. Currently Chairman and CEO of . Sinovation. Ventures and President of . Sinovation. Brief survey on optimization landscape for neural networks. Rong Ge. Duke University. Non-convex optimization. Theory: NP-hard. Practice: simple algorithms(SGD). Difficulties. Saddle Points. High-order Saddles. What’s new in ANNs in the last 5-10 years?. Deeper networks, . m. ore data, and faster training. Scalability and use of GPUs . ✔. Symbolic differentiation. ✔. reverse-mode automatic differentiation. Management and Radio Performance Improvement. Faris B. Mismar and Brian L. Evans. faris.mismar@utexas.edu. and . bevans@ece.utexas.edu. . MOTIVATION. Self-Organizing Networks. Cellular network faults impact SINR and data rates. Das. Computer Science and Engineering Department. Indian Institute of Technology Kharagpur. http://cse.iitkgp.ac.in/~. adas. /. Agenda. To brush up basics of Linear Algebra.. 06 Jan, 2022. CS. 60010. Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . New-Generation Models & Methodology for Advancing Speech Technology. Li Deng . Microsoft Research, Redmond, USA. Keynote at . Odyssey Speaker/Language Recognition Workshop. Singapore, June. 26, 2012.