Md Mahedi Hasan Mahedi-61

## cuda_11.3_installation_on_Ubuntu_20.04
#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

## installing_opencv_3.45_in_CentOS-7.sh
#!/bin/bash
# This gist is a step by step instructions to build and install OpenCV from source on CentOS 7
# note:  The easy and quick way to install is
# sudo yum install opencv opencv-devel opencv-python
# But this easy pypi installation can’t open video files on GNU/Linux distribution or on mac OS X system.
# And on some system opencv binaries provided packages are not compiled.
# Therefor we have no way rather than build it from source.


### first update and upgrade pre-install yum packages.

## net_config.sh
# first, install netctl if not already installed.
sudo pacman -S netctl

# to find out network card name; check
ip link


### configuring Static IP address
# my network card name is enp0s3. Now, copy the sample network card profile
sudo cp /etc/netctl/examples/ethernet-static /etc/netctl/enp0s3

## parity-3-problem
# 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)

## install_canon_printer.sh
## This gist contains step by step instructions of installing canon LBP3300 printer driver on ubuntu 18.04 machine
## After a couple of months painful trying, i finally got it successfully installed on my 18.04 ubuntu machine.

## Here is the requirement
# 1) For 64 bit linux version install necessary the libraries
# 2) install the official CAPT drivers
# 3) register the printer with lpadmin
# 4) register the printer with the ccpd daemon


## RBF_Pseudo_Inverse.py
"""
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

## CUDA_Toolkit_10.0_installation_on_CentOS_7.sh
#!/bin/bash
## This gist contains step by step instructions to install cuda v10.1 and cudnn 7.6 in CentOS 7
### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

## installing_opencv_from_source_in_Ubuntu_1804.sh
#!/bin/bash
# This gist is a step by step instructions to build and install OpenCV from source on ubuntu 18.04 LTS
# note:  The easy and quick way to install is
# sudo pip3 install opencv-python
# sudo pip3 install opencv-contrib-python
# But this easy pypi installation can’t open video files on GNU/Linux distribution or on mac OS X system.
# And on some system opencv binaries provided packages are not compiled.
# Therefor we have no way rather than build it from source.


## ml_setup.sh
#!/bin/bash
# Instructions for installing necessary packages for running machine learning projects in Ubuntu machine

# first update and upgrade your system
sudo apt-get update
sudo apt-get install build-essential cmake g++ gfortran git pkg-config python-dev python3-tk software-properties-common wget

# install pip3 for working on python 3
sudo apt-get install python3-pip
pip3 install --upgrade pip

## SqueezeNetCIFAR.py
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
	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check
	#!/bin/bash
	# This gist is a step by step instructions to build and install OpenCV from source on CentOS 7
	# note: The easy and quick way to install is
	# sudo yum install opencv opencv-devel opencv-python
	# But this easy pypi installation can’t open video files on GNU/Linux distribution or on mac OS X system.
	# And on some system opencv binaries provided packages are not compiled.
	# Therefor we have no way rather than build it from source.


	### first update and upgrade pre-install yum packages.
	# first, install netctl if not already installed.
	sudo pacman -S netctl

	# to find out network card name; check
	ip link


	### configuring Static IP address
	# my network card name is enp0s3. Now, copy the sample network card profile
	sudo cp /etc/netctl/examples/ethernet-static /etc/netctl/enp0s3
	# 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)
	## This gist contains step by step instructions of installing canon LBP3300 printer driver on ubuntu 18.04 machine
	## After a couple of months painful trying, i finally got it successfully installed on my 18.04 ubuntu machine.

	## Here is the requirement
	# 1) For 64 bit linux version install necessary the libraries
	# 2) install the official CAPT drivers
	# 3) register the printer with lpadmin
	# 4) register the printer with the ccpd daemon
	"""
	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
	#!/bin/bash
	# Instructions for installing necessary packages for running machine learning projects in Ubuntu machine

	# first update and upgrade your system
	sudo apt-get update
	sudo apt-get install build-essential cmake g++ gfortran git pkg-config python-dev python3-tk software-properties-common wget

	# install pip3 for working on python 3
	sudo apt-get install python3-pip
	pip3 install --upgrade pip
	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