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Borislav Kosharov nikibobi

  • Bulgaria
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@nikibobi
nikibobi / .vsconfig
Created Mar 5, 2019
Visual Studio Installer Configuration
View .vsconfig
{
"version": "1.0",
"components": [
"Microsoft.VisualStudio.Workload.ManagedDesktop",
"Microsoft.VisualStudio.Workload.NativeDesktop",
"Microsoft.VisualStudio.Workload.NetWeb",
"Microsoft.VisualStudio.Workload.ManagedGame",
"Microsoft.VisualStudio.Workload.NetCoreTools",
"microsoft.net.componentgroup.targetingpacks.common",
"microsoft.componentgroup.blend",
@nikibobi
nikibobi / gitea.yaml
Last active Jan 5, 2019
Gitea kubernetes config
View gitea.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: gitea
labels:
app: gitea
spec:
replicas: 1
selector:
matchLabels:
View тест.md
  1. Кои са целите на ЧМИ?

    • методологии и процеси за разработване на интерфейси
    • техники за оценяване и сравняване на интерфейси
    • методи за вложени интерфейси
    • разработване на нови интерфейси и техники на взаимодействие
    • разработване на описателни и предвиждащи модели и теории на взаимодействие
  2. Избройте фазите на информационния процесинг на човек

    1. Закодиране във форма за вътрешно представяне
    2. Сравняване със запомнено представяне
@nikibobi
nikibobi / fix-grub.sh
Created Nov 20, 2018
Personal shell script to fix EFI file when Windows updates
View fix-grub.sh
#!/bin/bash
set -x
cd /boot/EFI/
mv Microsoft/Boot/bootmgfw{,.orig}.efi
cp {grub/grubx64,Microsoft/Boot/bootmgfw}.efi
@nikibobi
nikibobi / colab-latex.py
Created Nov 3, 2018
Add LaTeX support to a Colab notebook for SymPy
View colab-latex.py
import sympy
def custom_latex_printer(exp, **options):
from google.colab.output._publish import javascript
url = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/latest.js?config=default"
javascript(url=url)
return sympy.printing.latex(exp, **options)
sympy.init_printing(use_latex="mathjax", latex_printer=custom_latex_printer)
View sympy-example.ipynb
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@nikibobi
nikibobi / pg-pong.py
Created Jul 27, 2018 — forked from karpathy/pg-pong.py
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
View pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
View landmarks.json
[
{
"distance": 79,
"name": "Policeman Monument",
"bearing": 84,
"latitude": 43.84599,
"image": "http://lh4.ggpht.com/pxXhWO9N2P06hsXgKABdT7MasbF6Rdv37bhJswycH4ELhE3_eZ58omN0cbmf3UBT17Vq1mlG49J-OehrL5a4eA",
"guid": "d407c66b765a4839ae04fd0c3f6d0539.16",
"compass": "E",
"longitude": 25.959249
@nikibobi
nikibobi / GithubRibbon.vue
Last active Nov 23, 2018
Github ribbon vue.js component
View GithubRibbon.vue
<template>
<a :href="link">
<img :src="image" :style="{ [this.orientation]: 0 }" alt="Fork me on GitHub">
</a>
</template>
<script lang="ts">
import { Component, Prop, Vue } from 'vue-property-decorator';
const palete = {
View Touchballs.glsl
#ifdef GL_FRAGMENT_PRECISION_HIGH
precision highp float;
#else
precision mediump float;
#endif
uniform vec2 resolution;
uniform int pointerCount;
uniform vec3 pointers[10];
uniform float battery;
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