Noon van der Silk silky

## annoying things in ml.md

      
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                silky
                / annoying things in ml.md
            
            
              Last active
              April 14, 2020 06:01
            
          
managing folders of data
saving experiments post-fact
waiting for stuff to run
easily seeing all your hyper-parameters
working out what you've done before
trying out different things, and saving them somehow
debugging models
wanting to visualise various outputs arbitrarily
doing things with streaming data?
loss landscape visualisation


## linear_diophantine.py
from collections import Counter
from fractions import Fraction


def _gcd(a, b):
	while a:
		a, b = b % a, a
	return b


## voronoi_indexed.hs
{-# LANGUAGE TupleSections #-}

module Main where

import Graphics.Gloss
import Graphics.Gloss.Raster.Field
import System.Random
import Data.Semigroup
import GHC.Base (NonEmpty(..))

## voronoi_indexed.hs
{-# LANGUAGE TupleSections #-}

module Main where

import Graphics.Gloss
import Graphics.Gloss.Raster.Field
import System.Random
import Data.Semigroup
import GHC.Base (NonEmpty(..))

## gc.c
// http://journal.stuffwithstuff.com/2013/12/08/babys-first-garbage-collector/
//

#define STACK_MAX 256
#define INITIAL_GC_THRESHOLD 2

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

void gc(); // Pre-Declared for co-recursive usage

## streamlit_prodigy.py
"""
Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
run simple training experiments for NER and text classification.

Requires the Prodigy annotation tool to be installed: https://prodi.gy
See here for details on Streamlit: https://streamlit.io.
"""
import streamlit as st
from prodigy.components.db import connect
from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size

## streamlit_prodigy.py
"""
Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
run simple training experiments for NER and text classification.

Requires the Prodigy annotation tool to be installed: https://prodi.gy
See here for details on Streamlit: https://streamlit.io.
"""
import streamlit as st
from prodigy.components.db import connect
from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size

## Install
pip install streamlit
pip install spacy
python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

## THLPE.hs
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
{-# LANGUAGE LambdaCase                 #-}
{-# LANGUAGE MultiWayIf                 #-}
{-# LANGUAGE RecordWildCards            #-}

module Game.THLPE
  ( THLPE ()
  , GodAnswer (..)
  , GodType (..)
  , GodName (..)

## clean-up-boot-partition-ubuntu.md

      
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                silky
                / clean-up-boot-partition-ubuntu.md
            
            
              Created
              December 22, 2018 02:31
                — forked from ipbastola/clean-up-boot-partition-ubuntu.md
            
              
                Safest way to clean up boot partition - Ubuntu 14.04LTS-x64, Ubuntu 16.04LTS-x64
              
          
    Safest way to clean up boot partition - Ubuntu 14.04LTS-x64, Ubuntu 16.04LTS-x64

Reference
Case I: if /boot is not 100% full and apt is working

1. Check the current kernel version

$ uname -r
	from collections import Counter
	from fractions import Fraction


	def _gcd(a, b):
	while a:
	a, b = b % a, a
	return b
	{-# LANGUAGE TupleSections #-}

	module Main where

	import Graphics.Gloss
	import Graphics.Gloss.Raster.Field
	import System.Random
	import Data.Semigroup
	import GHC.Base (NonEmpty(..))
	// http://journal.stuffwithstuff.com/2013/12/08/babys-first-garbage-collector/
	//

	#define STACK_MAX 256
	#define INITIAL_GC_THRESHOLD 2

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

	void gc(); // Pre-Declared for co-recursive usage
	"""
	Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
	run simple training experiments for NER and text classification.

	Requires the Prodigy annotation tool to be installed: https://prodi.gy
	See here for details on Streamlit: https://streamlit.io.
	"""
	import streamlit as st
	from prodigy.components.db import connect
	from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size
	pip install streamlit
	pip install spacy
	python -m spacy download en_core_web_sm
	python -m spacy download en_core_web_md
	python -m spacy download de_core_news_sm
	{-# LANGUAGE GeneralizedNewtypeDeriving #-}
	{-# LANGUAGE LambdaCase #-}
	{-# LANGUAGE MultiWayIf #-}
	{-# LANGUAGE RecordWildCards #-}

	module Game.THLPE
	( THLPE ()
	, GodAnswer (..)
	, GodType (..)
	, GodName (..)