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Adding machinelearning to a web app     Richard Dallaway @d6y
It’s easy, right?1.           Get some data2.    Find magic stats & algorithms3.         Predict the future!
It’s easy, right?                              that helps solve the problem   1.              Get some data    frame the p...
Make it easier forusers to #tag todos
tag
Can we suggest tagsas a task is typed in?
us e f u lCan we suggest tagsas a task is typed in?
Where to start? from “Algorithms of the Intelligent Web”
“Google uses Bayesian  filtering the way Microsoft uses the   if statement"            as told to Joel Spolsky
A contrived example    #home 40%           #work 60%“windows”   “fix”   “windows”   “fix”   50%      50%       16.6%     8...
A contrived example       #home 40%             #work 60%   “windows”   “fix”     “windows”   “fix”      50%      50%     ...
A contrived examplep (#h ome | “f i x ” )                #home 28%              #work 72%           “windows”     “fix”   ...
p(C | e) = P(C) x P(e | C)                P(e)
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 designin...
Demo:  addsharedo.comwith tag suggestions
results of running the model
It’s easy, right?1.          Explore the data2.        Frame your problem3. Measure the performance honestly
How to build?Write it yourself?Find a library?Ask Google to do it for you?    ...but pilot with offline data first.
www.manning.com40% off with bathcamp40   until August 10th
Questions?or later: @d6y richard@SpiralArm.com
Adding machine learning to a web app
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Adding machine learning to a web app

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A walk-through of how we approached adding tag suggestions to addsharedo.com

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Adding machine learning to a web app

  1. 1. Adding machinelearning to a web app Richard Dallaway @d6y
  2. 2. It’s easy, right?1. Get some data2. Find magic stats & algorithms3. Predict the future!
  3. 3. It’s easy, right? that helps solve the problem 1. Get some data frame the problem explore the data 2. Find magic stats & algorithms what’s success? pilot 3. Predict the future!does this help users? behaviour changes? UI impact?
  4. 4. Make it easier forusers to #tag todos
  5. 5. tag
  6. 6. Can we suggest tagsas a task is typed in?
  7. 7. us e f u lCan we suggest tagsas a task is typed in?
  8. 8. Where to start? from “Algorithms of the Intelligent Web”
  9. 9. “Google uses Bayesian filtering the way Microsoft uses the if statement" as told to Joel Spolsky
  10. 10. A contrived example #home 40% #work 60%“windows” “fix” “windows” “fix” 50% 50% 16.6% 83.4% p(#home | “fix”, “windows”) ?
  11. 11. A contrived example #home 40% #work 60% “windows” “fix” “windows” “fix” 50% 50% 16.6% 83.4%p(#home | “fix”) = 40% x 50% (40% x 50%) + (83.4% x 60%) = about 28%
  12. 12. A contrived examplep (#h ome | “f i x ” ) #home 28% #work 72% “windows” “fix” “windows” “fix” 50% 50% 16.6% 83.4% p(#home | “fix”, “windows”) = 28% x 50% (28% x 50%) + (16.6% x 72%) = about 55%
  13. 13. p(C | e) = P(C) x P(e | C) P(e)
  14. 14. 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”
  15. 15. Demo: addsharedo.comwith tag suggestions
  16. 16. results of running the model
  17. 17. It’s easy, right?1. Explore the data2. Frame your problem3. Measure the performance honestly
  18. 18. How to build?Write it yourself?Find a library?Ask Google to do it for you? ...but pilot with offline data first.
  19. 19. www.manning.com40% off with bathcamp40 until August 10th
  20. 20. Questions?or later: @d6y richard@SpiralArm.com

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