brian piercy bjpcjp

## d3js-bullet.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
margin: auto;
padding-top: 40px;
position: relative;
width: 800px;
}

## shell-best-practices.sh
#!/usr/bin/env bash

# source: https://sharats.me/posts/shell-script-best-practices/

set -o errexit
set -o nounset
set -o pipefail
if [[ "${TRACE-0}" == "1" ]]; then
    set -o xtrace
fi

## my first svg
<svg width="10" height="10">
  <rect x="0" y="0" width="10" height="10" fill="blue" />
</svg>

## pocket_json_prodmgmt_50_20220607
{"status"=>1, "complete"=>1, "list"=>{"3492948982"=>{"item_id"=>"3492948982", "resolved_id"=>"3492948982", "given_url"=>"https://www.practicalecommerce.com/tag/video-tools", "given_title"=>"", "favorite"=>"0", "status"=>"0", "time_added"=>"1638225235", "time_updated"=>"1654562695", "time_read"=>"0", "time_favorited"=>"0", "sort_id"=>0, "resolved_title"=>"Video Tools", "resolved_url"=>"https://www.practicalecommerce.com/tag/video-tools", "excerpt"=>"Video offers merchants a range of ways to engage customers and prospects. Here is a list of new and updated tools from video platforms and social media applications. There are tools for promoting products and producing shoppable videos and as well as for editing, live-streaming, and monetizing.", "is_article"=>"1", "is_index"=>"0", "has_video"=>"0", "has_image"=>"0", "word_count"=>"55", "lang"=>"en", "tags"=>{"ecommerce"=>{"item_id"=>"3492948982", "tag"=>"ecommerce"}, "prodmgmt"=>{"item_id"=>"3492948982", "tag"=>"prodmgmt"}, "tools"=>{"item_id"=>"3492948982", "tag"

## trading-investment-metrics.py
from blankly import Alpaca, CoinbasePro # supports stocks, crypto, and forex
import numpy as np
from math import sqrt


def cagr(start_value: float, end_value: float, years: int):
    return (end_value / start_value) ** (1.0 / years) - 1

def sharpe(account_values: np.array, risk_free_rate, annualize_coefficient):
    diff = np.diff(account_values, 1) / account_values[1:] # this gets our pct_return in the array

## approximate_nearest_neighbors.py
# Author: Tom Dupre la Tour
#
# License: BSD 3 clause
import time
import sys

try:
    import annoy
except ImportError:
    print("The package 'annoy' is required to run this example.")

## prodmgmt-art-of-profitability-booknotes.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                bjpcjp
                / prodmgmt-art-of-profitability-booknotes.md
            
            
              Last active
              May 14, 2019 21:07
            
          
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.


## dash-hello-world.py
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'),

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

## main.r
# 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/
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>
	body {
	font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
	margin: auto;
	padding-top: 40px;
	position: relative;
	width: 800px;
	}
	#!/usr/bin/env bash

	# source: https://sharats.me/posts/shell-script-best-practices/

	set -o errexit
	set -o nounset
	set -o pipefail
	if [[ "${TRACE-0}" == "1" ]]; then
	set -o xtrace
	fi
	<svg width="10" height="10">
	<rect x="0" y="0" width="10" height="10" fill="blue" />
	</svg>
	from blankly import Alpaca, CoinbasePro # supports stocks, crypto, and forex
	import numpy as np
	from math import sqrt


	def cagr(start_value: float, end_value: float, years: int):
	return (end_value / start_value) ** (1.0 / years) - 1

	def sharpe(account_values: np.array, risk_free_rate, annualize_coefficient):
	diff = np.diff(account_values, 1) / account_values[1:] # this gets our pct_return in the array
	# Author: Tom Dupre la Tour
	#
	# License: BSD 3 clause
	import time
	import sys

	try:
	import annoy
	except ImportError:
	print("The package 'annoy' is required to run this example.")
	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'),
	import sys
	reload(sys)
	sys.setdefaultencoding("utf-8")
	import requests
	import bs4
	import zipcode
	import threading
	import re
	import json
	import time
	# 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/