AndreiMoraru123

#include <print>
#include <tuple>

consteval auto Vector(int x, int y) {

    auto getX = [x] consteval {return x;};
    auto getY = [y] consteval {return y;};
    
    auto add = [x, y](auto other) consteval {
        const auto [otherX, otherY, _]  = other;

AndreiMoraru123

#include <stdio.h>
#include <stdlib.h>

struct Node {
  int data;
  struct Node *next;
  struct Node *prev;
};

void printList(struct Node *node) {

AndreiMoraru123

import cv2
import matplotlib.pyplot as plt

face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
profile_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_profileface.xml')
plate_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_russian_plate_number.xml')


def blur_regions(image, regions):
    for (x, y, w, h) in regions:

AndreiMoraru123

AndreiMoraru123

AndreiMoraru123

AndreiMoraru123

AndreiMoraru123

"""
A simple decision tree implementation in scikit-learn.
Trained on fabricated malware data.

Features: file id, name, size in MB, extension and executability.
Labels: malware or not.

Output:

    Accuracy:  0.75

AndreiMoraru123

"""
A simple implementation of L1 unstructured pruning.
Uses the magnitude of the weights to determine which weights to prune.
Made for the purpose of understanding pruning.
Built on top of the scikit-learn MLPClassifier.
Trained on the MNIST (28 x 28) dataset.

Output:

    Accuracy before pruning: 0.901