PPT-Gradient descent David Kauchak

Gradient descent David Kauchak CS 158 Fall 2019 Admin Assignment 3 almost graded Assignment 5 Course feedback An aside text classification Raw data labels Chardonnay Pinot Grigio Zinfandel Text raw data. How Yep Take derivative set equal to zero and try to solve for 1 2 2 3 df dx 1 22 2 2 4 2 df dx 0 2 4 2 2 12 32 Closed8722form solution 3 26 brPage 4br CS545 Gradient Descent Chuck Anderson Gradient Descent Parabola Examples in R Finding Mi This can be generalized to any dimension brPage 9br Example of 2D gradient pic of the MATLAB demo Illustration of the gradient in 2D Example of 2D gradient pic of the MATLAB demo Gradient descent works in 2D brPage 10br 10 Generalization to multiple Gradient descent is an iterative method that is given an initial point and follows the negative of the gradient in order to move the point toward a critical point which is hopefully the desired local minimum Again we are concerned with only local op Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. Bassily. Adam Smith . Abhradeep. Thakurta. . . . . Penn State . Yahoo! Labs. . Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. Methods for Weight Update in Neural Networks. Yujia Bao. Feb 28, 2017. Weight Update Frameworks. Goal: Minimize some loss function . with respect to the weights . ..  . input. layer. h. idden . layers. Perceptrons. Machine Learning. March 16, 2010. Last Time. Hidden Markov Models. Sequential modeling represented in a Graphical Model. 2. Today. Perceptrons. Leading to. Neural Networks. aka Multilayer . Grigory. . Yaroslavtsev. http://grigory.us. Lecture 8: . Gradient Descent. Slides at . http://grigory.us/big-data-class.html. Smooth Convex Optimization. Minimize . over . admits a minimizer . (. Lecture 4. September 12, 2016. School of Computer Science. Readings:. Murphy Ch. . 8.1-3, . 8.6. Elken (2014) Notes. 10-601 Introduction to Machine Learning. Slides:. Courtesy William Cohen. Reminders. Yujia Bao. Mar 1. , . 2017. Weight Update Frameworks. Goal: Minimize some loss function . with respect to the weights . ..  . input. layer. h. idden . layers. output . layer. …. Image credit: Joe . Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. Goals of Weeks 5-6. What is machine learning (ML) and when is it useful?. Intro to major techniques and applications. Give examples. How can CUDA help?. Departure from usual pattern: we will give the application first, and the CUDA later. CS 179: Lecture 13 Intro to Machine Learning Goals of Weeks 5-6 What is machine learning (ML) and when is it useful? Intro to major techniques and applications Give examples How can CUDA help? Departure from usual pattern: we will give the application first, and the CUDA later Deep Learning. Instructor: . Jared Saia. --- University of New Mexico. [These slides created by Dan Klein, Pieter . Abbeel. , . Anca. Dragan, Josh Hug for CS188 Intro to AI at UC Berkeley. All CS188 materials available at http://.