PPT-1 Lecture: Deep Networks Intro

Topics 1 st lecture wrapup difficulty training deep networks image classification problem using convolutions tricks to train deep networks Resources httpwwwcsutahedurajeevcs7960notes . Intro to IT. . COSC1078 Introduction to Information Technology. . Lecture 22. Internet Security. James Harland. james.harland@rmit.edu.au. Lecture 20: Internet. Intro to IT. . Introduction to IT. Booting. Intro to IT. . COSC1078 Introduction to Information Technology. . Lecture 15. Booting. James Harland. james.harland@rmit.edu.au. Lecture 15: Booting. Intro to IT. . Introduction. James Harland. . Intro to Applied Entomology, Lecture 19. I. Soil-applied & seed-treatment insecticides. Soil-applied for residual control:. Applied to kill insects in treated soil at time of application and for a period up to several weeks later; incorporated (at least lightly) or injected to mix with soil. Intro to IT. . COSC1078 Introduction to Information Technology. . Lecture 5. Audio. James Harland. james.harland@rmit.edu.au. Lecture . 5: Audio. Intro to IT. . Introduction. James Harland. Email:. 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. Reading and Research in Deep Learning. James K Baker. 11-364 Deep Learning R&R. Hands-on Tutorial Books with Sample Code. Leading Edge Research Papers. Background Tasks. Learn the core concepts of Deep Learning. 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!. . 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. . Materials with thanks to . Scott . Shenker. , . Jennifer Rexford, Ion . Stoica. , Vern . Paxson. and other colleagues at Princeton and UC Berkeley. Wireless. . – there is no cat!. "You see, wire telegraph is a kind of a very, very long cat. You pull his tail in New York and his head is meowing in Los Angeles. . 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. 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. Laks. V.S. . Lakshmanan. Department of Computer Science . University of British Columbia . http://www.cs.ubc.ca/~laks/534l/cpsc534l.html. Who I am, what I do, where/when you can find me . I do . data management .