This document contains summaries of several presentations and demos about the F# programming language. It discusses F# user groups and testimonials from various domains like machine learning and biotech. It also summarizes case studies on using F# for games and online advertising. Live demos are shown for type providers, machine learning algorithms, and accessing data from various sources. Resources mentioned include type providers, tutorials, books, and job trends in machine learning.

1. ALL YOUR TYPES ARE
BELONG TO US!
PHILLIP TRELFORD, @PTRELFORD
DDD DUNDEE 2013, #DUNDDD

2. F#UNCTIONAL LONDONERS
Meetup
Topics
• 600 members
• Finance
• 50 meetup
• Machine Learning
• Meets every 2 weeks
• Big Data
• Talks & Hands On
• Gaming

3. FSHARP.ORG/GROUPS

4. F# TESTIMONIALS – MACHINE
LEARNING
PHILLIP TRELFORD, @PTRELFORD
DDD DUNDEE 2013, #DUNDDD

5. FSHARP.ORG/TESTIMONIALS
For a machine learning scientist, speed of experimentation is the critical factor to optimize.
Compiling is fast but loading large amounts of data in memory takes a long time.
With F#’s REPL, you only need to load the data once
and you can then code and explore in the interactive environment.
Unlike C# and C++, F# was designed for this mode of interaction.
- Patrice Simard, Microsoft

6. FSHARP.ORG/TESTIMONIALS - AMYRIS
BIOTECH
F# has been phenomenally useful.
I would be writing a lot of this in Python otherwise
and F# is more robust, 20x - 100x faster to run
and for anything but the most trivial programs,
faster to develop.
- Darren Platt, Amyris Biotechnology

7. CASE STUDIES
PHILLIP TRELFORD, @PTRELFORD
DDD DUNDEE 2013, #DUNDDD

8. F# TOOLS FOR HALO 3
Questions
• Controllable player skill distribution (slow down!)
• Controllable skills distributions (re-ordering)
Simulations
• Large scale simulation of 8,000,000,000 matches
• Distributed computation – 15 machines for 2wks
Tools
• Result viewer (Logged results: 52GB of data)
• Real-time simulator of partial update

9. ADCENTER
Weeks of data in training:
• 7,000,000,000 impressions
2 weeks of CPU time during sessions
• 2 wks x 7 days x 86,400 sec/day
Learning algorithm speed requirement:
• 5,787 impression updates /sec
• 172.8 µs per impression update

10. LIVE DEMOS
PHILLIP TRELFORD, @PTRELFORD
DDD DUNDEE 2013, #DUNDDD

11. TYPE PROVIDERS: JSON
open FSharp.Data
type Simple = JsonProvider<“sample.js”>
let simple = Simple.Parse(""" { "name":"Tomas", "age":4 } """)
simple.Age

12. CSV TYPE PROVIDER

13. SPLIT DATA SET (FROM ML IN ACTION)
Python
def splitDataSet(dataSet, axis, value):
retDataSet = []
for featVec in dataSet:
if featVec[axis] == value:
reducedFeatVec = featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)
return retDataSet
F#
let splitDataSet(dataSet, axis, value) =
[|for featVec in dataSet do
if featVec.[axis] = value then
yield featVec |> Array.removeAt axis|]

14. K-MEANS CLUSTERING ALGORITHM
(* K-Means Algorithm *)
/// Group all the vectors by the nearest center.
let classify centroids vectors =
vectors |> Array.groupBy (fun v -> centroids |> Array.minBy (distance v))
/// Repeatedly classify the vectors, starting with the seed centroids
let computeCentroids seed vectors =
seed |> Seq.iterate (fun centers -> classify centers vectors
|> Array.map (snd >> average))

15. R – TYPE PROVIDER

16. WORLD BANK DATA

17. RESOURCES
PHILLIP TRELFORD, @PTRELFORD
DDD DUNDEE 2013, #DUNDDD

18. TYPE PROVIDERS
• JSON
• XML
• CSV
• Excel
• SQL
• R
• MATLAB
• Hadoop
• ...

19. TRYFSHARP.ORG

20. BUY THE BOOK

21. GET THE T-SHIRT

22. MACHINE LEARNING JOB TRENDS
• Source indeed.co.uk

23. QUESTIONS
PHILLIP TRELFORD, @PTRELFORD
DDD DUNDEE 2013, #DUNDDD