Learning How To Learn

## unixnano.go
package main

import (
	"log"
	"time"
)

func main() {

	var counter int

## KJV_Spacy_.idea_KJV_Spacy.iml
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$" />
    <orderEntry type="jdk" jdkName="Python 3.5.2 (~/anaconda/bin/python)" jdkType="Python SDK" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
  <component name="TestRunnerService">
    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
  </component>

## how_to_learn.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
              Xeoncross
            / how_to_learn.md
          
          
            Last active Sep 14, 2017
          
          
            Notes by https://pastebin.com/JNbGxvpQ of how to learn well
          
        
      View how_to_learn.md
  
    
    Learning How To Learn
Module 1 - What is Learning
Focused/Diffuse Modes Thinking

Obviously ‘focused’ is when you’re concentrating. Direct approach to solving familiar problems.
Focused: thoughts move through nicely-paved road of familiar notions (neural pattern looks very tight and directed).

encompasses rational, sequential, analytical approaches to thinking


Diffuse: More of a search function neural pattern. Thoughts move widely. More of a broad/big-picture perspective trying to connect ideas from different places.
We’re always either in focused or diffuse mode of thinking.


## hugo.list.tests.html
{{ partial "head.html" . }}
   <body>


      {{ partial "nav.html" . }}

			<main>
      	<article id="content">

					<!-- <pre>{{ printf "%#v" . }}</pre> -->

## py-nltk-svo.py
# -*- coding: utf-8 -*-
import os
import nltk
from nltk.tree import *
from nltk.parse import stanford
import nltk.data
import nltk.draw
import os
import sys

## knn_word2vec_example.py
# -*- coding: utf-8 -*-
"""
Improving approximate nearest neighbour search with k-nearest neigbors.
Using sklearn-KDTree here just for demonstration. You can plugin much faster
nearest neigbour search implementations (flann, annoy to name a few) for
better results. For benchmarks, check out:
1) Radim Řehůřek (author of gensim) -
    http://rare-technologies.com/performance-shootout-of-nearest-neighbours-intro
2) Erik Bernhardsson (author of annoy) -
    https://github.com/erikbern/ann-benchmarks

## Common Objects of Verbs; Thesaurus For Main Topics.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
              Xeoncross
            / Common Objects of Verbs; Thesaurus For Main Topics.ipynb
          
          
            Created Jul 11, 2017
              — forked from Anand-/Common Objects of Verbs; Thesaurus For Main Topics.ipynb
          
        
      View Common Objects of Verbs; Thesaurus For Main Topics.ipynb
  
    
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      Viewer requires iframe.
    
  
## Gensim.py
from gensim.utils import simple_preprocess

tokenize = lambda x: simple_preprocess(x)
# tokenize("We can load the vocabulary from the JSON file, and generate a reverse mapping (from index to word, so that we can decode an encoded string if we want)?!")

import os
import json
import numpy as np
from gensim.models import Word2Vec

## stem_lemma_pos_nltk_example.py
from nltk import pos_tag
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer, WordNetLemmatizer

stemmer = PorterStemmer()
lemmatiser = WordNetLemmatizer()

print("Stem %s: %s" % ("going", stemmer.stem("going")))
print("Stem %s: %s" % ("gone", stemmer.stem("gone")))
print("Stem %s: %s" % ("goes", stemmer.stem("goes")))

## facebook_headless_login_using_google_chrome.js
//install dev vesrion of google chrome form https://www.chromium.org/getting-involved/dev-channel
//install chrome-remote-interface from npm npm install chrome-remote-interface
const CDP = require('chrome-remote-interface');

CDP((protocol) => {
    const {Page, Runtime} = protocol;

// First, need to enable the domains we're going to use.
Promise.all([
    Page.enable(),