View unixnano.go
package main
import (
"log"
"time"
)
func main() {
var counter int
View 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>
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.
View hugo.list.tests.html
{{ partial "head.html" . }}
<body>
{{ partial "nav.html" . }}
<main>
<article id="content">
<!-- <pre>{{ printf "%#v" . }}</pre> -->
View 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
View 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
View Common Objects of Verbs; Thesaurus For Main Topics.ipynb
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View 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
View 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")))
View 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(),