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# solving parity-3 problems using numpy only | |
from os import error | |
import numpy as np | |
import math | |
np.random.seed(1) | |
def relu(x): | |
return np.maximum(0, x) |
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""" | |
Course: Applications of NN (CpE - 520) | |
Homework Assignment 9 | |
""" | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
import torch |
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""" | |
Script to implement simple self organizing map | |
@author: Riley Smith | |
Created: 1-27-21 | |
""" | |
""" | |
@modified: Md Mahedi Hasan (11/28/2021) | |
For Homework Assingment 11 (CpE 520 | |
""" |
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""" | |
Author: Md Mahedi Hasan | |
For Homework Assingment 12 (CpE 520) | |
""" | |
import numpy as np | |
import os | |
import matplotlib.pyplot as plt | |
from torch.utils.data import DataLoader | |
from torchvision import transforms |
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""" | |
modified from several gist of https://gist.github.com/tarlanahad | |
For Homework Assingment 12 (CpE 520) | |
""" | |
import numpy as np | |
import torchvision | |
from matplotlib import pyplot as plt | |
from sklearn.metrics import confusion_matrix | |
import seaborn as sns |
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import torch | |
import torch.optim as optim | |
import numpy as np | |
from matplotlib import pyplot as plt | |
from tabnanny import check | |
from sklearn.metrics import confusion_matrix | |
import seaborn as sns | |
from torch import nn | |
import torch.nn.init as init | |
from torchvision import transforms, datasets |
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