'Stochastic gradient descent' presentation slideshows

CSM6120 Introduction to Intelligent Systems

CSM6120 Introduction to Intelligent Systems. Neural Networks. Sub-symbolic learning. When we use some sort of rule-based system, generally we understand the rules We understand the conclusions it draws, because it can tell us

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Learning

Learning. computation making predictions choosing actions acquiring episodes statistics algorithm gradient ascent (eg of the likelihood) correlation Kalman filtering implementation flavours of Hebbian synaptic plasticity neuromodulation. Forms of Learning. supervised learning

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Function Learning and Neural Nets

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Modeling Real Graphs using Kronecker Multiplication

Modeling Real Graphs using Kronecker Multiplication. Jure Leskovec, Christos Faloutsos Machine Learning Department. Modeling large networks. Large networks ( e.g. , web, internet, on-line social networks) with millions of nodes Need statistical methods and models to quantify large networks.

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Artificial Neural Networks

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Clustering

Clustering. Hypothesis: Hebbian synaptic plasticity enables a perceptron to compute the mean of its preferred stimuli. Unsupervised learning. Sequence of data vectors Learn something about their structure Multivariate statistics Neural network algorithms Brain models.

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Matrix Factorization

Matrix Factorization. Bamshad Mobasher DePaul University. The $1 Million Question. Ratings Data. 17,700 movies. 480,000 users. Training Data. 100 million ratings (matrix is 99% sparse) Rating = [user, movie-id, time-stamp, rating value] Generated by users between Oct 1998 and Dec 2005

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Stochastic Gradient Descent Training for L1-regularizaed Log-linear Models with Cumulative Penalty

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Some tips for effective map reducing

Some tips for effective map reducing. CHRISTOPHER SEVERS eBay. THE AGENDA. THE AGENDA. Quick survey of the current landscape for Hadoop tools A light comparison of the best functional tools. General advice Some code samples . The Alternatives . I promise this part will be quick.

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The delta rule

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Parameter-Free Spatial Data Mining Using MDL.

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Semi-Stochastic Gradient Descent Methods

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Gang Wang Derek Hoiem David Forsyth

Learning Image Similarity from Flick r Groups Using Stochastic Intersection Kernel Machines ICCV 2009, UIUC. Gang Wang Derek Hoiem David Forsyth. OUTLINE. INTRODUCTION APROACH (implement detail) EXPERIMENTS CONCLUSION. Introduction.

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Machine Learning ICS 178

Machine Learning ICS 178. Instructor: Max Welling . What is Expected?. Class Homework/Projects (40%) Midterm (20%) Final (40%). For the projects, students should make teams. This class needs your active participation: please ask questions and

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Lecture 5

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Lecture 5

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Algorithms for Data Analytics

Algorithms for Data Analytics. B.Ramamurthy. Data Analytics (Data Science). Intuition/ understanding. Big-data analytics StatsAlgs. Data. *. Discoveries/ intelligence. Statistical Inference. EDA. Decisions/ Answers/ Results. *. Three T ypes of Data Science Algorithms.

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Recitation 1 April 9

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