aialenti

Hakky54

OpenSSL 🔐

Install

Install the OpenSSL on Debian based systems

sudo apt-get install openssl

raulqf

How to install OpenCV 4.5.2 with CUDA 11.2 and CUDNN 8.2 in Ubuntu 22.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

oguiza

maxpagels

# Python implementation of the EXP3 (Exponential weight for Exploration and Exploitation)
# algorithm for solving adversarial bandit problems. Based on the original paper:
# http://rob.schapire.net/papers/AuerCeFrSc01.pdf

import numpy as np
import time

np.random.seed(12345)

n_arms = 4

maxpagels

import math
import types
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
from tqdm import tqdm


def df_to_vw_regression(df, filepath='in.txt', sample_weights=None, columns=None, target=None, namespace='namespace'):
    if columns is None:
        columns = df.columns.tolist()

mkocabas

import torch

# Original author: Francisco Massa:
# https://github.com/fmassa/object-detection.torch
# Ported to PyTorch by Max deGroot (02/01/2017)
def nms(boxes, scores, overlap=0.5, top_k=200):
    """Apply non-maximum suppression at test time to avoid detecting too many
    overlapping bounding boxes for a given object.
    Args:
        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].

Cartexius

Reference - https://www.eriksmistad.no/getting-started-with-google-test-on-ubuntu/

sudo apt-get install libgtest-dev

sudo apt-get install cmake # install cmake

cd /usr/src/gtest

sudo cmake CMakeLists.txt

Yegorov

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# https://toster.ru/q/72866
# How to
# wget http://gist.github.com/...
# chmod +x ya.py
# ./ya.py download_url path/to/directory

import os, sys, json

kairess

def conv2d_bn(x, filters, kernel_size, strides=1, padding='same', activation='relu', use_bias=False, name=None):
  x = Conv2D(filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias, kernel_initializer='he_normal', name=name)(x)
  if not use_bias:
    bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
    bn_name = None if name is None else name + '_bn'
    x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
  return x

def mfm(x):
  shape = K.int_shape(x)