PPT-Deep Q-Learning for Self-Organizing Networks Fault

Management and Radio Performance Improvement Faris B Mismar and Brian L Evans farismismarutexasedu and bevanseceutexasedu MOTIVATION SelfOrganizing Networks Cellular network faults impact SINR and data rates. Information Processing & Artificial Intelligence. New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 4, 2013 (Day 3). 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). 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. 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. 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?. 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. The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. 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!. New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 2-5, 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . 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. 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.) . 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/ . Outline. What is Deep Learning. Tensors: Data Structures for Deep Learning. Multilayer Perceptron. Activation Functions for Deep Learning. Model Training in Deep Learning. Regularization for Deep Learning. Eli Gutin. MIT 15.S60. (adapted from 2016 course by Iain Dunning). Goals today. Go over basics of neural nets. Introduce . TensorFlow. Introduce . Deep Learning. Look at key applications. Practice coding in Python.