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Introduction

If anyone is up for creating a car number plate detector based on machine learning (such as neural networks), tons of images for training is required. There is good amount of data available online for training number plate detectors. However, here's a problem - they do not work very well with Bangladeshi number plates; it's because Bangladeshi number plates are quite different from British, American, or even Indian ones. Images of Bangladeshi number plates are extremely limited on Google, and no dataset containing such images is publicly available. This calls for the need of creating a dataset containing photos of Bangladeshi number plates, which will be released publicly for free. This is intended to help everyone from beginners to machine learning to data scientists. Our work is intended to be primarily used for training neural networks.

Instructions

To create a good dataset, the data needs to be diverse and distinguishable. Too similar data can be misleading to a neural network. Here ar

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diskutil list

sudo mkdir /Volumes/NTFS
sudo umount /dev/disk0s3
sudo ntfs-3g /dev/disk0s3 /Volumes/NTFS -olocal -oallow_other

nahiyan

Controls: Elevator, Ailerons, Rudder, Flaps, and throttle (5 channels).

nahiyan

.MODEL SMALL
.DATA
    NUMBERS DB 4 DUP (?)
    J DW 1
    I DW ?
    LINEBREAK DB 10, 13, "$"
    KEY DB ?
    
    MSG1 DB "Ascending Order: $"
    MSG2 DB "Descending Order: $"

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#include <stdio.h>

int abs(int value) {
    if (value < 0)
        return value * -1;
    else
        return value;
}

int even(int value) {

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#include <stdio.h>

int main()
{
    // Number of test cases
    int n;
    scanf("%d", &n);

    // Minimum distances
    int distances[n];

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#include <stdio.h>
#include <string.h>

int main() {
    int n, i, j, flag, order;
    int x[20], y[20];
    scanf("%d", & n);
    for (i = 0; i < n; i++) {
        scanf("%d", & order);
        order--;

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Character vector created by brgfx - www.freepik.com

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org 100h

; Set the address
MOV AX, 3010h
MOV DS, AX
MOV BX, 0000h

; Move the contents of the selected address to BL
MOV BL, BYTE PTR DS:[BX]

nahiyan

• All of Darwin's dependencies (cocos2d-x, OpenNN, FlatBuffers, SQLite3, wxWidgets, etc) are merged into the repository. This is to reduce burden on the developer to manually build the libraries themselves. Instead, CMake handles building the dependencies. Moreover the dependencies are modified to harmonize with each other and prevent conflicts such as duplicate symbols.

• Darwin now comprises a single executable. Previously there was a separate executable for each extension. Instead, a GUI-based Control Panel is created to manage everything - from resuming evolution to choosing extensions.

• wxWidgets is used to create the Control Panel's GUI. It's cross-platform and has a friendly API.

• OpenNN's randomization isn't seeded properly by default, resulting in "randomized" neural network parameters (weights and biases) being the same all the time. To resolve this issue, we used our own randomization techniques.

• Rotation of Jumpers is disabled to prevent them from falling over to the ground.