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Original notes by James Clear

• Do the math yourself. Too many people take numbers from unreliable sources.

• There are 4 levels of learning: Awareness, Awkwardness, Application, Assimilation

• Customer-Solution Profit: Know your customers incredibly well and create a solution specifically for them.

bjpcjp

import dash
import dash_core_components as dcc 
import dash_html_components as html 

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H1(children='Hello Dash'),

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import sys
reload(sys)
sys.setdefaultencoding("utf-8")
import requests
import bs4
import zipcode
import threading
import re
import json
import time

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# How to build your own NN classifier in r
# source: http://www.r-bloggers.com/build-your-own-neural-network-classifier-in-r/
# reference: http://junma5.weebly.com/data-blog/build-your-own-neural-network-classifier-in-r

# project:
# 1) build simple NN with 2 fully-connected layers
# 2) use NN to classify a dataset of 4-class 2D images & visualize decision boundary.
# 3) train NN with MNIST dataset

# ref: stanford CS23 source: http://cs231n.github.io/

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# source: http://multithreaded.stitchfix.com/blog/2016/01/13/market-watch/
# https://github.com/klarsen1/MarketMatching

# from terminal command line:
    sudo apt-get install libcurl4-openssl-dev

# from R command line:
# install CausalImpact package:

    install.packages("devtools")

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import scrapy

class StackOverflowSpider(scrapy.Spider):
	name = 'stackoverflow'
	start_urls = ['http://stackoverflow.com/questions?sort=votes']

	def parse(self, response):

		for href in response.css('.question-summary h3 a::attr(href)'):

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1) Make a download dir to store the node source and download it.

    mkdir downloads
    cd downloads
    git clone https://github.com/joyent/node.git
    Find the latest version

2) List all of the tags in the repository, and check out the most recent.
    git tag
    git checkout v0.9.9