10-bert 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 […]
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School of Computing and Information Systems The University of Melbourne COMP90042 NATURAL LANGUAGE PROCESSING (Semester 1, 2021) Workshop exercises: Week 7 Discussion 1. What are contextual representations? 2. How does a transformer captures dependencies between words? What advan- tages does it have compared to RNN? 3. What is discourse segmentation? What do the segments consist
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11-machine-translation Neural Machine Translation¶ In this workshop, we are going to build a seq2seq machine translation model and train it on a parallel corpus of English and French. We will frame the translation problem in a slightly different way. Instead of translating the sentence word by word, we are going to work on character-level. This
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l7-feedforward-v3 COPYRIGHT 2021, THE UNIVERSITY OF MELBOURNE 1 COMP90042 Natural Language Processing Lecture 7 Semester 1 2021 Week 4 Jey Han Lau Deep Learning for NLP: Feedforward Networks COMP90042 L7 2 Outline • Feedforward Neural Networks Basics • Applications in NLP • Convolutional Networks COMP90042 L7 3 Deep Learning • A branch of machine learning
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By Timo Betcke © Copyright 2020. Previous Assignment 3 – Sparse matrix formats on GPUs Assignment 4 – Time-dependent problems Submission Deadline: We 15 December, 10am Consider a square plate with sides . At time t = 0 we are heating the plate up such that the temperature is on one side and on
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Memory Hierarchy Design pCache Organization pVirtual Memory pSix Basic Cache Optimizations Copyright By PowCoder代写 加微信 powcoder p2.4 Ten Advanced Optimizations of Cache Performance pMemory Technology and Optimizations pVirtual Memory and Protection pProtection: Virtual Memory and Virtual Machines Memory Hierarchy Design Ten Advanced Optimizations p The previous six basic optimizations try to reduce miss rate, miss
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JavaFx COMP2511 JavaFX Prepared by Dr. Ashesh Mahidadia JavaFX 2COMP2511: JavaFX JavaFX • Java’s original GUI library was the Abstract Window Toolkit (AWT). • Swing was added to the platform in Java SE 1.2. • JavaFX is Java’s GUI, graphics and multimedia API. • JavaFX has better threading support, uses the GPU (graphics processing unit)
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L4_Representations L4: Mesh and Point Cloud Hao Su Machine Learning meets Geometry Shape Representation: Origin- and Application-Dependent • Acquired real-world objects • Modeling “by hand” • Procedural modeling • … 2 Rasterized form (regular grids) Geometric form (irregular) Point Cloud Mesh Implicit Shape F(x) = 0 Volumetric Multi-view Depth Map Other than parametric representations,
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Microsoft PowerPoint – L11-GPUs-Reduction+Shuffle.pptx University of Toronto Mississauga, Department of Mathematical and Computational Sciences CSC 367 Parallel Programming Bogdan Simion General-purpose computing with Graphics Processing Units (GPUs) Comprehensive examples – Reductions With many thanks to NVIDIA’s Mark Harris for some of the neat CUDA examples! University of Toronto Mississauga, Department of Mathematical and Computational Sciences
Assignment 3 – Sparse matrix formats on GPUs Submission Deadline: Mo 29 November, 10am Task 1: So far we learned about the CSR format. On CPUs this is a widely used standard format. However, it has some severe disadvantages on GPUs, but also on modern vector extensions (AVX, etc.) of CPUs. The paper Improving the
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