ELEC5307 Deep Learning Project #1: Parameters in Neural Networks Due: 11 Oct 2019 11:59PM 1 Ob jectives This laboratory aims to introduce the basic techniques in deep neural networks. In this labo- ratory you will: • Learn to use PyTorch to load images and train a neural network for classification. • Understand the functions of […]
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Python实用 检查TensorFlow推荐引擎的准确性。 Tensorflow是一种流行的机器学习框架。 人们普遍认为它可以在计算机上利用GPU使其速度非常快。 TF-Recomm是一个基于tensorflow的电影推荐引擎，它使用分解模型和来自movielens的数据集。 原始代码https://github.com/songgc/TF-recomm没有要求建议的界面。 Andy创建了一个版本，用于侦听用户编号的端口，并将返回该用户的建议。 代码绝不是完美的，可能有错误！ 代码捆绑在Docker容器中以方便使用。 要使用此代码： 标准小版本 从https://github.com/acobley/TF-recomm.git克隆项目 git clone https://github.com/acobley/TF-recomm.git 运行docker容器： docker run –name runtfrecomm -p 81:81 -i -t acobley / tfrecomm bash 这将启动一个docker容器，并使用命令行打开它。 切换到TF-recomm目录并运行引擎 python svd_train_val.py 这将训练模型，一旦完成等待套接字连接。 打开另一个窗口并打开一个telnet会话（如果你在Windows上，你将需要一个）： telnet localhost 81 类型和数字在1到约5000之间，您应该得到建议。 GPU大版本 此版本使用更大的数据集，应该使用您的GPU。 此版本尚未经过测试，请确保小版本首先运行。 像以前一样克隆项目，但切换到GPU分支 git checkout GPU 运行docker容器： docker run –name runtfrecommgpu -p 81:81 -i -t acobley
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Practical Block-wise Neural Network Architecture Generation Zhao Zhong1,3∗, Junjie Yan2,Wei Wu2,Jing Shao2,Cheng-Lin Liu1,3,4 1National Laboratory of Pattern Recognition,Institute of Automation, Chinese Academy of Sciences 2 SenseTime Research 3 University of Chinese Academy of Sciences 4 CAS Center for Excellence of Brain Science and Intelligence Technology Email: {zhao.zhong, liucl}@nlpr.ia.ac.cn, {yanjunjie, wuwei, shaojing}@sensetime.com Abstract Convolutional neural networks
COMP528 Assignment Resits (2018/19) Dr. Michael K. Bane Overview • 4 assignments, each worth 10% of total your letter will indicate which (if any) assignments you are expected to resit • Resits questions comparable to original, testing same learning etc you will get lots of hints and help by going back to
DLP project pg. 1 DATA LEVEL PARALLELISM 05/24/19 DLP Project (DUE: Friday. 6/7/2019, 11:59PM Learn the data-parallel programming paradigm and become familiar with CUDA by implementing an algorithm on the GPU. DLP project pg. 2 Data Level Parallelism BACKGROUND In this project, you will do template matching on a GPU. Template matching is used to
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Deep Learning: Coursework 3¶ Student Name: (Student Number: ) Start date: 26th March 2019 Due date: 29th April 2019, 09:00 am How to Submit¶ When you have completed the exercises and everything has finished running, click on ‘File’ in the menu-bar and then ‘Download .ipynb’. This file must be submitted to Moodle named as studentnumber_DL_cw3.ipynb
Operating Systems Lecture 8a Dr Ronald Grau School of Engineering and Informatics Spring term 2018 Previously 1 Memory management Addressing and address spaces Partitioning and segmentation Virtual memory Paging Today 2 Memory management Page replacement Recap: Virtual memory 3 Objectives Hide physical memory Memory protection Illusion of
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CSCI3180 – Principles of Programming Languages – Spring 2018 Assignment 3 — Dynamic Scoping and Perl Deadline: Apr 8, 2018 (Sunday) 23:59 1 Introduction The purpose of this assignment is to offer you a first experience with Perl, which supports both dynamic scoping and static scoping. Our main focus is dynamic scoping. The assignment consists
April 2018 Final Examination EXAMINER: Compiler Design COMP-520 14:00 – 17:00 April 30, 2018 Alexander Jacob Krolik ASSOC. EXAMINER: INSTRUCTIONS Prof. Clark Verbrugge STUDENT NAME: McGILL ID: X CLOSED BOOK OPEN BOOK SINGLE-SIDED PRINTED ON BOTH SIDES OF THE PAGE X X EXAM: MULTIPLE CHOICE Note: The Examination Security Monitor Program detects pairs of students
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Introduction to Mobile Application Development, Android Overview (Authors: Dr Caspar Ryan, Dr Ermyas Abebe, Mr Keith Foster) Semester 1, 2019 COSC2309/2347 Mobile Application Development Topic 1, Slide 1 Mobile Application Development • Course Leader/Lecturer: Dr. Caspar Ryan • Email: caspar.ryan@rmit.edu.au • Enforced Prerequisite: – Software Architecture: Design and Implementation OR Advanced Programming Semester 1, 2019