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Oh Lord! Bestow me with Knowledge

Md Mahedi Hasan Mahedi-61

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Oh Lord! Bestow me with Knowledge
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Mahedi-61 / parity-3-problem
Last active January 10, 2023 21:00
Solving Parity-3 problem using 3-layer from scratch
# 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)
@Mahedi-61
Mahedi-61 / autoencoder_mnist.py
Last active November 18, 2021 20:47
3-layer autoencoder for MNIST digit reconstruction and classification with visualization (Pytorch)
"""
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
@Mahedi-61
Mahedi-61 / KSOM.py
Created November 29, 2021 01:24
Implementing Kohonen Self Organizing Maps using numpy
"""
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
"""
@Mahedi-61
Mahedi-61 / RBF_classifier.py
Last active December 5, 2021 21:50
Radial Basis Function Netowork for MNIST classification tranined on gradient decent approach
"""
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
@Mahedi-61
Mahedi-61 / RBF_Pseudo_Inverse.py
Last active October 3, 2022 21:26
Radial Basis Function initialized on K-means and trained on Pseudo Inverse for MNIST digit classification
"""
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
@Mahedi-61
Mahedi-61 / SqueezeNetCIFAR.py
Created February 1, 2022 04:24
SqueezeNet on CIFAR-10 Dataset
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