manashmandal

const axios = require("axios");
const endpoint = "https://randomuser.me/api";
const totalRequests = 20;
(async () => {
  let requests = [];
  for (let i = 0; i < totalRequests; i++) {
    requests.push(axios.get(endpoint));
  }
  Promise.all(requests)
    .then((responses) => {

manashmandal

[
  {
    "Name": "chevrolet chevelle malibu",
    "Miles_per_Gallon": 18,
    "Cylinders": 8,
    "Displacement": 307,
    "Horsepower": 130,
    "Weight_in_lbs": 3504,
    "Acceleration": 12,
    "Year": "1970-01-01",

manashmandal

ssh -i key.pem -R remote_open_port:localhost:local_open_port user@domain

manashmandal

import torch
import torch.nn as nn
from torch.autograd import Variable

batch_size = 3
max_length = 3
hidden_size = 2
n_layers =1

# container

manashmandal

sudo apt-get install -y python3.8 python3.8-dev
rm -rf /app/tradingbot/venv
cd /app/tradingbot
virtualenv venv --python /usr/bin/python3.8

manashmandal

# Change extension of files
for f in $(ls); do mv $f $(echo $f | cut -d "." -f 1).jpeg ; done

manashmandal

# Day 0: Mean, Median, and Mode

# Selecting standard input
con <- file('stdin', open='r')

# We don't need the first input 
data_line <- readLines(con)[[2]]

# splitting the data into individual string
split_data <- strsplit(data_line, " ")

manashmandal

#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include "jsmn.h"

#define JSON_FILE_PATH "../Parser/test.json"
#define BUFFER_SIZE 5000
#define MAX_TOKEN_COUNT 128

// Read files

manashmandal

This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for 
native compiling without any issues.
Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing that out.
Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.

1.Run the following commands in both machines to install the necessary libraries etc.(mine worked with them,so they should be enough 
hopefully)
sudo apt-get update && sudo apt-get upgrade

manashmandal

def build_deep_autoencoder(img_shape,code_size=32):
    """PCA's deeper brother. See instructions above"""
    H,W,C = img_shape
    
    encoder = keras.models.Sequential()
    encoder.add(L.InputLayer(img_shape))

    encoder.add(L.Flatten())
    encoder.add(L.Dense(code_size*8, activation='relu'))
    encoder.add(L.Dense(code_size*4, activation='tanh'))