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name: Master CI/CD

on: 
  push:
    branches:
      - master
jobs:
  primary:
    runs-on: ubuntu-latest
   

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service: sample-github-actions

provider:
  name: aws
  runtime: nodejs12.x
  stage: dev
  region: us-east-1

functions:
  app:

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var express = require('express');
var app = express();
var serverlessHttp = require('serverless-http')


app.get("/", function(req, res){
    res.send("HELLO WORLD");
})

module.exports.handler = serverlessHttp(app)

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import numpy as np

input1 = [0,0,1,1]
input2 = [1,0,1,0]
truth = [0,1,0,1]

weight1 = 0
weight2 = -1
bias = 0
def perceptron(input1, input2):

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import numpy as np

input1 = [0,0,1,1]
input2 = [1,0,1,0]
truth = [0,0,1,0]

weight1 = 1
weight2 = 1
bias = -2
def perceptron(input1, input2):

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def lis_dp(X):
    if not X:
        return 0
    memo = [1] * len(X)
    for i in range(1,len(X)):
        for j in range(i):
            if X[j] < X[i]:
                memo[i] = max(memo[i],memo[j]+1)
    return max(memo)

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def lcs(X,Y, m, n):
    if m < 0 or n < 0:
        return 0
    elif X[m] == Y[n]:
        return 1 + lcs(X,Y, m-1,n-1)
    else:
        return max(lcs(X,Y,m-1,n),lcs(X,Y,m,n-1))

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def maxCount(arr):
    prevMax = 0
    currMax = 0
    for ar in arr:
        temp = currMax
        currMax = max(prevMax+ar, currMax)
        prevMax = temp
    return currMax

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{
  "author": "Vigneash Sundararajan",
  "name": "sample",
  "version":"0.0.1"
}

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import torch  
#Creates two tensor objects 
#Where X is a simple 1 Dimentional Tensor 
#Y is a vector
X = torch.tensor(1.0)
Y = torch.tensor([1.0,2.0])
#Alternatively we can also create a tensor from data like this 
Z = torch.tensor([[1.0,2.0,3.0],
                  [2.0,3.0,4.0],
                  [3.0,4.0,5.0]])