Lab06 POS Tagging POS tagging is the process of marking up a word in a corpus to a corresponding part of speech tag, based on its context and definition. This task is not straightforward, as a particular word may have a different part of speech based on the context in which the word is used […]
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Lab08 Statistical Language Model (SLM) A statistical language model is a probability distribution over sequences of words. Given such a sequence, say of length m, it assigns a probability $P(w_1, \ldots, w_m)$ to the whole sequence. One model solution is to make the assumption that the probability distribution for a word depends only on the
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GPU Programming – Exercise 1: Getting started 1 Introduction This exercise gives a gentle introduction to CUDA programming using a very simple code. The main objectives in this exercise are to learn about: • thewayinwhichanapplicationconsistsofahostcodetobeexecutedontheCPU,withkernelcode executed on the GPU, Copyright By PowCoder代写 加微信 powcoder • howtocopydatabetweenthegraphicscard(device)andtheCPU(host), • howtoincludeerrorchecking,andprintingfromakernel. The exercise can be done either on
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COM4521/COM6521: Parallel Computing with Graphical Processing Units (GPUs) Assignment (80%) Deadline: 17:00 Monday 17th May (week 12) Last Edited: 26/02/2021 Document Changes: Any corrections or changes to this document will be noted here and an update will be sent out to the course google group mailing list. Introduction The assessment has been designed against the
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License OSU’s College of Engineering has six Nvidia DGX-2 systems Each DGX server: • Has 16 NVidia Tesla V100 GPUs Copyright By PowCoder代写 加微信 powcoder • Has 28TB of disk, all SSD • Has two 24-core Intel Xeon 8168 Platinum 2.7GHz CPUs • Has
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In questions where you have to write code, minor syntax problems will not be penalised, as long as it is clear what you mean. It is a written exam, we do not expect to run your code. 1. (a) State Amdahl’s law and show how it is derived. (b) Suppose that the speedup obtained over
RNN with LSTM¶ In [1]: import torch import torch.nn.functional as F from torchtext import data from torchtext import datasets import time import random import pandas as pd torch.backends.cudnn.deterministic = True General Settings¶ In [2]: RANDOM_SEED = 123 torch.manual_seed(RANDOM_SEED) VOCABULARY_SIZE = 20000 LEARNING_RATE = 1e-3 #
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Dropout¶ In [ ]: from torchvision import datasets from torchvision import transforms from torch.utils.data import DataLoader from torch.utils.data import SubsetRandomSampler import torch.nn.functional as F import matplotlib.pyplot as plt import torch import time In [ ]: %matplotlib inline Settings and Dataset¶ In [ ]: ########################## ### SETTINGS ########################## RANDOM_SEED = 123 BATCH_SIZE = 256 NUM_HIDDEN_1 = 75 NUM_HIDDEN_2 = 65 DROP_PROBA =
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Multilayer Perceptron in Python with Sigmoid Output and MSE Loss¶ Imports¶ In [1]: import matplotlib.pyplot as plt import pandas as pd import torch %matplotlib inline In [4]: import time import numpy as np from torchvision import datasets from torchvision import transforms from torch.utils.data import DataLoader import torch.nn.functional as F import torch Settings and Dataset¶ In [ ]: ########################## ###
MLP With Different Loss Functions¶ Imports¶ In [ ]: import matplotlib.pyplot as plt import pandas as pd import torch %matplotlib inline In [13]: import time import numpy as np from torchvision import datasets from torchvision import transforms from torch.utils.data import DataLoader import torch.nn.functional as F import torch Settings and Dataset¶ In [15]: !wget www.di.ens.fr/~lelarge/MNIST.tar.gz !tar -zxvf MNIST.tar.gz ########################## ###
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