Adding machine
learning to a web app
Richard Dallaway @d6y
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It’s easy, right?
1. Get some data
2. Find magic stats & algorithms
3. Predict the future!
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It’s easy, right?
1. Get some data
2. Find magic stats & algorithms
3. Predict the future!
that helps solve the problem
frame the problem explore the data
pilot
what’s success?
does this help users?
UI impact?
behaviour changes?
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Make it easier for
users to #tag todos
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No content
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tag
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Can we suggest tags
as a task is typed in?
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Can we suggest tags
as a task is typed in?
useful
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Where to start?
from “Algorithms of the Intelligent Web”
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“Google uses Bayesian
filtering the way
Microsoft uses the
if statement"
as told to Joel Spolsky
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A contrived example
#home 40% #work 60%
“windows”
50%
“fix”
50%
“windows”
16.6%
“fix”
83.4%
p(#home | “fix”, “windows”) ?
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A contrived example
#home 40% #work 60%
“windows”
50%
“fix”
50%
“windows”
16.6%
“fix”
83.4%
p(#home | “fix”) = 40% x 50%
(40% x 50%) + (83.4% x 60%)
= about 28%
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A contrived example
#home 28% #work 72%
“windows”
50%
“fix”
50%
“windows”
16.6%
“fix”
83.4%
p(#home | “fix”, “windows”) = 28% x 50%
(28% x 50%) + (16.6% x 72%)
= about 55%
p(#home | “fix”)
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p(C | e) = P(C) x P(e | C)
P(e)
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p(C | e) = P(C) x P(e | C)
P(e)
“the estimation of P(e | C) can be viewed as the
central issue in designing learning systems. ”
— Weiss & Kulikowski
“Computer Systems that Learn”
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Demo:
addsharedo.com
with tag suggestions
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results of running the model
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It’s easy, right?
1. Explore the data
2. Frame your problem
3. Measure the performance honestly
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Write it yourself?
Find a library?
Ask Google to do it for you?
How to build?
...but pilot with offline data first.
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