#include #include #include #include double *function_a(const double *A, const double *x, const int N) { double *y = new double[N]; for (unsigned int i = 0; i < N; i++) { for (unsigned int i = 0; i < N; i++) { for (unsigned int j = 0; j < N; j++) { y[i] += […]
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Coursework: Performance Modelling, Vectorisation and GPU Programming Module: Performance Modelling, Vectorisation and GPU Programming (COMP 52315) Term: Epiphany term, 2024 Lecturer: Anne Reinarz1 and Laura Morgenstern2 Submission Please submit a zip-directory containing all files required for this assignment via the Gradescope submission point provided via Blackboard Learn Ultra at https://blackboard.dur ham.ac.uk/ultra/courses/_54359_1/outline. Deadlines Consult the MISCADA
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Microsoft Word – Assignment1_2024.docx ATHK1001 ANALYTIC THINKING: ASSIGNMENT 1, 2024 Due date: 11:59pm Thursday, March 28th (Week 6). Late penalty of 5% per calendar day applies. Online submission: All submissions are to be made online via the link on the ATHK1001 Canvas website. All submissions must be a single PDF file. Do not submit files
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COMP9021 Principles of Programming Term 1, 2024 Assignment 1 Worth 13marks and due Week 7 Monday @ 10am 1. General Matters The purpose of this assignment is to: • develop your problem-solving skills. • design and implement the solution to a problem in the form of a medium sized Python program. • practice the use
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UNSW ECON2209 Assessment Project UNSW ECON2209 Assessment At the start of an R session for this course, remember to type library(fpp3) in the R Studio Console. This will then load (most of) the R packages you will need, including some data sets. • Total value: 25 marks. • Submission is due on Friday of Week
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Urban Transport Masters Programme Transport Planning Lab Assignment 3 The deadline for submission is noon on March 21st 2024. In this assignment you should utilise the data and materials provided in the lab session ‘Network analysis and location modelling’ (also available on GitHub: https://github.com/rafavdz/routing_tuto- You should address Question 1 and Question 2 of this brief,
COMP9417 – Machine Learning Homework 2: Numerical Implementation of Logistic Regression Introduction In homework 1, we considered Gradient Descent (and coordinate descent) for minimizing a regularized loss function. In this homework, we consider an alternative method known as Newton’s algorithm. We will first run Newton’s algorithm on a simple toy problem, and then implement it
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# Assessment 4 – ReactJS: BigBrain 1. Background & Motivation 2. The Task (Frontend) 3. The Support (Backend) 4. Constraints & Assumptions 5. Teamwork 6. Marking Criteria 7. Originality of Work 8. Submission 9. Late Submission Policy ## 0. Change Log * 02/04/2023: General edit clause added to 2.2.2 * 02/04/2023: Comments about advancing added
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# Assessment 3 – Vanilla JS: Qanda 1. Background & Motivation 2. The Task 3. Getting Started 4. Constraints & Assumptions 5. Marking Criteria 6. Originality of Work 7. Submission 8. Late Submission Policy ## 0. Change Log * 11/03: Fixed up due date / Moved `insertAdjacentHTML from able to use to prohibited` * 11/03:
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ENGG1811 Assignment 1: Pattern detection (Term 1, 2024) Due date: 5pm, Thursday of Week-07 . Late Penalty: Late submissions will be penalised at the rate of 0.2% per hour for 121 hours. Followed by 75.8% per hour for 1 hour. Submissions will not be accepted after 9am Tuesday of Week-08. Change Log 16/03/24 – Added
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