Fine-tuning with BERT¶ In this workshop, we’ll learn how to use a pre-trained BERT model for a sentiment analysis task. We’ll be using the pytorch framework, and huggingface’s transformers library, which provides a suite of transformer models with a consistent interface. Note: You may find certain parts of the code difficult to follow. This is […]
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Squishy Maps for Soft Body Modelling Using Generalised Chain Mail KIT308/408 (Advanced) Multicore Architecture and Programming Advanced CPU Architecture Dr. Ian Lewis Discipline of ICT, School of TED University of Tasmania, Australia 1 Introduce the concept of pipelining Understand why it a primary feature of high-performance architecture design Introduce basic concepts of superscalar architectures See
Squishy Maps for Soft Body Modelling Using Generalised Chain Mail KIT308/408 (Advanced) Multicore Architecture and Programming Exam Dr. Ian Lewis Discipline of ICT, School of TED University of Tasmania, Australia 1 A MyLO exam this year Very different from previous’ years But can still use previous years for study So I’ve included details about how
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PowerPoint Presentation Change the Jv(x) function in Bessel–Fourier moments to Radial Harmonic Fourier moment is the Tn(r) function as Parallel Computing with GPU There are six kernel functions used in our experiments: RADIUS_KERNEL, ARCTAN_KERNEL, Tn_KERNEL, EXP_KERNEL, MATRIX_KERNEL, RECONSTRUCT_KERNEL. 2 Parallel Computing with GPU RADIUS_KERNEL and ARCTAN_KERNEL Used to calculate radius values r and angular values
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COMP5046 Assignment 1¶ Make sure you change the file name with your unikey. Readme¶ If there is something to be noted for the user, please mention here. If you are planning to implement a program with Object Oriented Programming style Visualising the comparison of different results is a good way to justify your decision. 1
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10: The OpenGL Pipeline 18: Pipeline Optimisation COMP5822M: High Performance Graphics Pipeline Optimisation First rule of optimisation: Don’t optimise Second rule of optimisation: Find the bottleneck first Pipeline optimisation: Find the slowest stage in the pipeline Look for stalling threads COMP5822M: High Performance Graphics Profiling Tools PIX for Windows (DirectX) gDEBugger (OpenGL) NVPerfKit (NVIDIA) GPUPerfStudio
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COMP5900X Assignment 2 (Supplementary Materials)¶ Use this code to answer the questions in Assignment 2 Part 1. Sentiment Analysis of IMDB Dataset¶ In the following, we’ll be building a machine learning model to detect sentiment (i.e. detect if a sentence is positive or negative). This will be done on movie reviews, using the IMDb dataset.
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1 Introduction to Graphics Programming ITP4710 2D/3D Graphics Programming 01 Introduction to Graphics Programming On completion of the module, students are expected to be able to: analysis the differences in different graphics programming environment; develop 2D and 3D graphics programs for general gaming purposes; apply animation effects in developing 2D graphics and/or game programs; apply
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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
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10: The OpenGL Pipeline 17: Deferred Rendering COMP5822M: High Performance Graphics Forward Rendering The standard form of projective rendering Every fragment does lighting calculation Expensive bottleneck in practice COMP5822M: High Performance Graphics Deferred Rendering Reduces bottleneck of lighting Single lighting computation per pixel COMP5822M: High Performance Graphics Deferring Pass First pass doesn’t compute lighting It
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