Adding machine learning to a web app
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The document discusses adding machine learning capabilities to a web application. It notes that adding machine learning is not as simple as it may seem at first and outlines three key steps: 1) Get and explore data, 2) Frame the problem by defining what success looks like, and 3) Measure performance honestly. It provides examples of using Bayesian filtering to suggest tags as users type tasks and discusses piloting models with offline data before full implementation.
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Adding machine learning to a web app
- 1. Adding machine learning to a web app Richard Dallaway @d6y
- 2. It’s easy, right? 1. Get some data 2. Find magic stats & algorithms 3. Predict the future!
- 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. Make it easier for users to #tag todos
- 6. tag
- 7. Can we suggest tags as a task is typed in?
- 8. us e f u l Can we suggest tags as a task is typed in?
- 9. Where to start? from “Algorithms of the Intelligent Web”
- 10. “Google uses Bayesian filtering the way Microsoft uses the if statement" as told to Joel Spolsky
- 11. A contrived example #home 40% #work 60% “windows” “fix” “windows” “fix” 50% 50% 16.6% 83.4% p(#home | “fix”, “windows”) ?
- 12. 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%
- 13. A contrived example p (#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%
- 14. p(C | e) = P(C) x P(e | C) P(e)
- 15. 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”
- 16. Demo: addsharedo.com with tag suggestions
- 17. results of running the model
- 18. It’s easy, right? 1. Explore the data 2. Frame your problem 3. Measure the performance honestly
- 19. How to build? Write it yourself? Find a library? Ask Google to do it for you? ...but pilot with offline data first.
- 20. www.manning.com 40% off with bathcamp40 until August 10th
- 21. Questions? or later: @d6y richard@SpiralArm.com