Neural Networks Dr. Thompson March 26, 2013. Artificial Neural Network Topology.

Slides:



Advertisements
Similar presentations
Objective - To graph linear equations using the slope and y-intercept.
Advertisements

Unit 6 – Fundamentals of Calculus Section 6
Linear Equations Review. Find the slope and y intercept: y + x = -1.
STROUD Worked examples and exercises are in the text PROGRAMME F6 POLYNOMIAL EQUATIONS.
Equation of a Tangent Line
Calculus 2413 Ch 3 Section 1 Slope, Tangent Lines, and Derivatives.
Derivatives - Equation of the Tangent Line Now that we can find the slope of the tangent line of a function at a given point, we need to find the equation.
Artificial Neural Networks
Notes Over 6.9Writing a Cubic Function Write the cubic function whose graph is shown.
1 CALCULUS Even more graphing problems
Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.
Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.
Clicker Question 1 What is an equation of the tangent line to the curve f (x ) = x 2 at the point (1, 1)? A. y = 2x B. y = 2 C. y = 2x 2 D. y = 2x + 1.
Implicit Differentiation. Objectives Students will be able to Calculate derivative of function defined implicitly. Determine the slope of the tangent.
1 1 Slide Simple Linear Regression Chapter 14 BA 303 – Spring 2011.
Ranga Rodrigo April 5, 2014 Most of the sides are from the Matlab tutorial. 1.
Section 6.1: Euler’s Method. Local Linearity and Differential Equations Slope at (2,0): Tangent line at (2,0): Not a good approximation. Consider smaller.
Classification Part 3: Artificial Neural Networks
11 CSE 4705 Artificial Intelligence Jinbo Bi Department of Computer Science & Engineering
Classification / Regression Neural Networks 2
MECN 3500 Inter - Bayamon Lecture 9 Numerical Methods for Engineering MECN 3500 Professor: Dr. Omar E. Meza Castillo
From Machine Learning to Deep Learning. Topics that I will Cover (subject to some minor adjustment) Week 2: Introduction to Deep Learning Week 3: Logistic.
Statistical Methods Statistical Methods Descriptive Inferential
Jeff Howbert Introduction to Machine Learning Winter Regression Linear Regression.
Y=a+bx Sum of squares of errors Linear Regression: Method of Least Squares The Method of Least Squares is a procedure to determine the best fit line to.
Neural Networks Dr. Thompson March 19, Artificial Intelligence Robotics Computer Vision & Speech Recognition Expert Systems Pattern Recognition.
A note about gradient descent: Consider the function f(x)=(x-x 0 ) 2 Its derivative is: By gradient descent. x0x0 + -
Jeff Howbert Introduction to Machine Learning Winter Regression Linear Regression Regression Trees.
Derivatives Test Review Calculus. What is the limit equation used to calculate the derivative of a function?
All the World’s a Polynomial Chris Harrow
Curve Fitting Pertemuan 10 Matakuliah: S0262-Analisis Numerik Tahun: 2010.
y=a+bx Linear Regression: Method of Least Squares slope y intercept y
Specialist Mathematics Polynomials Week 3. Graphs of Cubic Polynomials.
MA Day 34- February 22, 2013 Review for test #2 Chapter 11: Differential Multivariable Calculus.
Modelleerimine ja Juhtimine Tehisnärvivõrgudega Identification and Control with artificial neural networks.
CS 551/651 Search and “Through the Lens” Lecture 13 Search and “Through the Lens” Lecture 13.
Section 3.9 Linear Approximation and the Derivative.
OLS Regression What is it? Closely allied with correlation – interested in the strength of the linear relationship between two variables One variable is.
Business Calculus Derivative Definition. 1.4 The Derivative The mathematical name of the formula is the derivative of f with respect to x. This is the.
MTH 253 Calculus (Other Topics) Chapter 11 – Infinite Sequences and Series Section 11.8 –Taylor and Maclaurin Series Copyright © 2009 by Ron Wallace, all.
Learning: Neural Networks Artificial Intelligence CMSC February 3, 2005.
Fitting Equations to Data Chapter 7. Problems Classwork: 7.1, 7.8, 7.13, 7.22 Homework: 7.2, 7.9, 7.16, 7.23, 7.28.
Learning with Neural Networks Artificial Intelligence CMSC February 19, 2002.
Linear Approximations. In this section we’re going to take a look at an application not of derivatives but of the tangent line to a function. Of course,
Notes on Weighted Least Squares Straight line Fit Passing Through The Origin Amarjeet Bhullar November 14, 2008.
Fitting Equations to Data
Artificial neural networks
Modelleerimine ja Juhtimine Tehisnärvivõrgudega
CSE 473 Introduction to Artificial Intelligence Neural Networks
Announcements HW4 due today (11:59pm) HW5 out today (due 11/17 11:59pm)
with Daniel L. Silver, Ph.D. Christian Frey, BBA April 11-12, 2017
Machine Learning – Regression David Fenyő
CSE 473 Introduction to Artificial Intelligence Neural Networks
The Derivative and the Tangent Line Problems
Goodfellow: Chap 6 Deep Feedforward Networks
y=a+bx Linear Regression: Method of Least Squares slope y intercept y
Collaborative Filtering Matrix Factorization Approach
1. Use the quadratic formula to find all real zeros of the second-degree polynomial
Linear regression Fitting a straight line to observations.
Indicate all x- and y-intercepts on the graph of the function y = x Choose the correct answer from the following: x-intercept (4,0), y-intercept.
Least Squares Fitting A mathematical procedure for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the.
EXIT TICKET: Graphing Linear Equations 11/17/2016
Nonlinear Fitting.
Sigmoid and logistic regression
Discrete Least Squares Approximation
Name:______________________________
Neural Networks II Chen Gao Virginia Tech ECE-5424G / CS-5824
y=a+bx Linear Regression: Method of Least Squares slope y intercept
Artificial Intelligence 10. Neural Networks
Neural Networks II Chen Gao Virginia Tech ECE-5424G / CS-5824
Presentation transcript:

Neural Networks Dr. Thompson March 26, 2013

Artificial Neural Network Topology

Artificial Neuron Activation

Threshhold Functions (include graphs) Linear Logistic Hyperbolic Tangent – Sigmoid (*) Step Logistic Curve

Network Output Y = f(WX) Z = f(W’Y) = f(W’f(WX))

Error Correction (Method of Least Squares) Minimize Total Error = E = Σ (Z-O) 2 using Partial Differentiation (Calculus III)

Error Function: Local & Global Minima

Back Propagation

Least Squares Tutorial Linear Regression Derive optimum slope and intercept How do we do Quadratic? Cubic? Why not nth Degree Polynomial? Overfitting

Learning & Testing Matlab Examples Simple Classes Thyroid Breast Cancer

Questions?