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Do the math yourself. Too many people take numbers from unreliable sources.
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There are 4 levels of learning: Awareness, Awkwardness, Application, Assimilation
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Customer-Solution Profit: Know your customers incredibly well and create a solution specifically for them.
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#!/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 |
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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 |
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# 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.") |
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# Rails 6.0.2.3 | |
# 'Behavior' = std Rails scaffold with one name:string attribute. | |
# rails db:migrate = no problem. | |
# rails test = 2 fails below. other controllers (~60, identical except for object name) pass. | |
Error: | |
BehaviorsControllerTest#test_should_destroy_behavior: | |
NoMethodError: undefined method `count' for ActionDispatch::IntegrationTest::Behavior:Module | |
test/controllers/behaviors_controller_test.rb:42:in `block in <class:BehaviorsControllerTest>' | |
test/controllers/behaviors_controller_test.rb:42:in `block in <class:BehaviorsControllerTest>' |
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31: from -e:1:in `<main>' | |
30: from /home/<myname>/.rbenv/versions/2.5.1/lib/ruby/site_ruby/2.5.0/rubygems/core_ext/kernel_require.rb:72:in `require' | |
29: from /home/<myname>/.rbenv/versions/2.5.1/lib/ruby/site_ruby/2.5.0/rubygems/core_ext/kernel_require.rb:72:in `require' | |
28: from /home/<myname>/.rbenv/versions/2.5.1/lib/ruby/gems/2.5.0/gems/bootsnap-1.4.6/lib/bootsnap/load_path_cache/core_ext/kernel_require.rb:55:in `load' | |
27: from /home/<myname>/.rbenv/versions/2.5.1/lib/ruby/gems/2.5.0/gems/bootsnap-1.4.6/lib/bootsnap/load_path_cache/core_ext/kernel_require.rb:55:in `load' | |
26: from /home/<myname>/projects/ideas/factory-inabox/v20200501/bin/rails:9:in `<top (required)>' | |
25: from /home/<myname>/.rbenv/versions/2.5.1/lib/ruby/gems/2.5.0/gems/zeitwerk-2.3.0/lib/zeitwerk/kernel.rb:23:in `require' | |
24: from /home/<myname>/.rbenv/versions/2.5.1/lib/ruby/gems/2.5.0/gems/bootsnap-1.4.6/lib/bootsnap/load_path_cache/core_ext/kernel_require.rb:31:in `require' | |
23: from /home/<myname>/.rbenv/versions/2.5.1/lib/ru |
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Bundler, the underlying system Vagrant uses to install plugins, | |
reported an error. The error is shown below. These errors are usually | |
caused by misconfigured plugin installations or transient network | |
issues. The error from Bundler is: | |
conflicting dependencies fog-core (~> 1.43.0) and fog-core (= 1.45.0) | |
Activated fog-core-1.45.0 | |
which does not match conflicting dependency (~> 1.43.0) | |
Conflicting dependency chains: |
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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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