Floating-Point Arithmetic ECS130 Winter 2017 January 27, 2017 Floating point numbers I Floating-point representation of numbers (scientific notation) has four components, for example, − 3.1416× 101 ↑ sign ↑ significand ↑ base ← exponent I Computers use binary (base 2) representation, each digit is the integer 0 or 1, e.g. 10110 = 1× 24 + […]
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1 Table of Contents ………………………………………………………………………………………………………………………. 1 Problem 1: …………………………………………………………………………………………………………. 1 Problem 2: …………………………………………………………………………………………………………. 3 Problem 3 ………………………………………………………………………………………………………….. 4 Problem 4 ………………………………………………………………………………………………………….. 6 % Academic License % Engineering 006 Homework % % These programs, solves the 4 problems in homework 2 prompt. % % Eng 6, Spring 2016 % Name: % Student ID: % Lab Section:
handoutE.dvi ECS130 Scientific Computing Handout E February 13, 2017 1. The Power Method (a) Pseudocode: Power Iteration Given an initial vector u0, i = 0 repeat ti+1 = Aui ui+1 = ti+1/‖ti+1‖2 (approximate eigenvector) θi+1 = u H i+1Aui+1 (approximate eigenvalue) i = i+ 1 until convergence (b) Practical stopping criterion: |θi+1 − θi| ≤
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Effective Approaches to Attention-based Neural Machine Translation Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412–1421, Lisbon, Portugal, 17-21 September 2015. c©2015 Association for Computational Linguistics. Effective Approaches to Attention-based Neural Machine Translation Minh-Thang Luong Hieu Pham Christopher D. Manning Computer Science Department, Stanford University, Stanford, CA 94305 {lmthang,hyhieu,manning}@stanford.edu Abstract
Homework 8 Due April 7, 2017 at 11:59 p.m. There are 5 time slots that you can utilize to transmit the data. You may transmit the data quickly using just one time slot or you may transmit the data slower using all 5 time slots or any number of time slots between 1 and 5.
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Unconstrained Optimization I Optimization problem Given f : Rn −→ R find x∗ ∈ Rn, such that x∗ = argmin x f(x) I Global minimum and local minimum I Optimality Necessary condition: ∇f(x∗) = 0 Sufficient condition: Hf (x∗) = ∇2f(x∗) is positive definite Newton’s method I Taylor series approximation of f at k-th iterate
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1 Table of Contents ………………………………………………………………………………………………………………………. 1 Item 1: Good habits: ……………………………………………………………………………………………… 1 Item 2: Define the variable ‘t’ to be an array that starts at 0 and ends at 2*pi with enough interme- diate values so that the plots will look smooth. ……………………………………………………………… 2 Item 3: Evaluate sine and cosine from 0 to 2*pi. …………………………………………………………….
hw2.dvi ECS130 Homework Assignment #2 Due: 4:00pm, Feburary 3, 2017 1. A real symmetric matrix A = AT is positive definite if any of the following equivalent condi- tions hold: • The quadratic form xTAx > 0 for all nonzero vectors x. • All determinants formed from submatrices of any order centered on the diagonal
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INT301 Assessment Task • Assessment Task (80 marks) In this assessment, you are to implement several algorithms to classify the characters segmented from the license plates of automobiles using MATLAB. The raw data are provided in ass2data.zip. Be sure to design your program in a general, well-structured fashion, and document the code appropriately. • Implement
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Department of Electrical Engineering and Electronics ELEC362 Project Linear regression application Module Coursework name Component weight Semester HE Level Lab location Work *Estimated time to finish Assessment method Submission format Submission deadline Late submission Resit opportunity Marking policy Anonymous marking Feedback Expected release of marks date Learning outcomes ELEC362 Project 50% 1 5 personal computers/laptops,