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#Convolutional neural network to process images of dogs and cats

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator

classifier = Sequential()

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import pandas as pd
dataset = pd.read_csv("Churn_Modelling.csv")

X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import make_column_transformer

LabelEncoder_X_1 = LabelEncoder()

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# recurrent neural network to predict stock exchange data

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset_train = pd.read_csv('Download/Google_Stock_Price_Train.csv')
dataset_test = pd.read_csv('Download/Google_Stock_Price_Test.csv')
training_set = dataset_train.iloc[:,1:2].values

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#Self Organizing Maps to detect frauds in credit card applications

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('Credit_Card_Applications.csv')

X = dataset.iloc[:, :-1].values
y = dataset.iloc[:,-1].values

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# Write csv in R
df <- data.frame(name = c("Jon", "Winston", "Suzy"),
                 age = c(23, 41, 32))
write.csv(df,"C:\\Users\\Matheus\\Desktop\\MyData.csv", row.names = FALSE)


# Read csv with R
data <- read.csv("input.csv")
print(data)

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using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class ObjectTransform : MonoBehaviour
{
    // Start is called before the first frame update

    private Vector3 startPosition;
    public bool changePosition, moveInCircles;

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using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class objectControl : MonoBehaviour
{
    public float speed = 3.0f;
    void Update()
    {

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Command	Description
docker build -t myimage:1.0	Build an image from the Dockerfile in the current directory and tag the image
docker image ls	List all images that are locally stored with the Docker Engine
docker image rm alpine:3.4	Delete an image from the local image store

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=begin

With bundler/inline there is no need to use external gems 
it lets you download/install/activate gems directly in the script 
skipping the need to handle bundle install or call it through bundle exec

=end


require 'bundler/inline'

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# Installing the Package:

pip install googleDriveFileDownloader

# Sample code:

from googleDriveFileDownloader import googleDriveFileDownloader
gdownloader = googleDriveFileDownloader()
gdownloader.downloadFile("https://drive.google.com/uc?id=1O4x8rwGJAh8gRo8sjm0kuKFf6vCEm93G&export=download")