PPT-Backpropagation and Neural Nets

EECS 442 David Fouhey Fall 2019 University of Michigan httpwebeecsumichedufouheyteachingEECS442F19 MidSemester Checkin Things are busy and stressful Take care of yourselves and remember that grades are important but the objective function of life really isnt sumofsquaredgrades. 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. 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. 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. Ashutosh. Pandey and . Shashank. . S. rikant. Layout of talk. Classification problem. Idea of gradient descent . Neural network architecture. Learning a function using neural network. Backpropagation algorithm. Introduction to Computer Vision. Basics of Neural Networks, and. Training Neural Nets I. Connelly Barnes. Overview. Simple neural networks. Perceptron. Feedforward. neural networks. Multilayer . perceptron and properties. 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 . 15. October 19, 2016. School of Computer Science. Readings:. Bishop . Ch. . 5. Murphy Ch. 16.5, Ch. 28. Mitchell Ch. 4. 10-601B Introduction to Machine Learning. Reminders. 2. Outline. Logistic Regression (Recap). Neural networks are sequences of parametrized functions. conv. filters. subsample. subsample. conv. linear. filters. weights. Parameters.  . x.  . Why backpropagation. Neural networks are sequences of parametrized functions. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. 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 . Greg Lewis (MSR and NBER). Matt Taddy (MSR and Chicago). Goal. To work out how to use instrumental variables for counterfactual prediction using (arbitrary) machine learners. To explore the practicalities of implementing this approach using deep neural nets. Zachary . C. Lipton . zlipton@cs.ucsd.edu. Time. . series. Definition. ：. A.  time series is a series of . data. . points.  indexed (or listed or graphed) in time order. . It . is a sequence of . ECE6504 – Deep Learning for Perception Ashwin Kalyan V Introduction to CAFFE (C) Dhruv Batra 2 Logistic Regression as a Cascade (C) Dhruv Batra 3 Slide Credit: Marc'Aurelio Ranzato , Yann LeCun Gary Cottrell. Computer Science and Engineering Department. Institute for Neural Computation. Temporal Dynamics of Learning Center. UCSD. 4/11/17. CSE 87. 2. Introduction. Your brain is made up of 10.