Machine Learning Live

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Presentation slides from my Machine Learning Live talk at the 2012 Clojure Conj

Presentation slides from my Machine Learning Live talk at the 2012 Clojure Conj

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  • 1. Machine Learning Live Clojure Conj 2012 Mike Anderson @mikera
  • 2. Machine Learning – A definition "Field of study that gives computers the ability to learn without being explicitly programmed.“ Arthur Samuel, 1959Source: Good Old Wikipedia 2
  • 3. Learning = Building functions from experienceTask Input OutputSimple mathematical x y = sin(x)functionSpam filtering Text of an email Probability of email being message spam (%)Stockmarket Historical data on Expected priceprediction - Stock prices movements - Economic indicatorsRemembering Thought: Thought:names “Who was that guy “Oh yes – that was Bob” who liked windsurfing? 3
  • 4. The state of machine learning“It works! sort of…. sometimes…. on a good day….” 4
  • 5. nuroko.comWe’re building a toolkit for machine learning that is:• General purpose – works on any data• Powerful – advanced algorithms to detect complex patterns• Scalable – handle unlimited data at internet scale• Realtime – suitable for online use in real applications• Pragmatic – designed for solving real problems 5
  • 6. Why Clojure? Productivity and fun! Good parts of the JVMREPL Interactive experiments Functional programming DSLs with composable abstractions 6
  • 7. Some Key AbstractionsVector 1 0 1 1 0 Efficiently represents information as a vector of double values Converts arbitrary data into vectors (andCoder “Cat” 1 0 1 1 0 back again!) 𝑜𝑢𝑡𝑝𝑢𝑡 = 𝑓 𝑖𝑛𝑝𝑢𝑡 Represents a problem to solve – typicallyTask via provision of training examples Represents a functionModule - (e.g. a Neural Network)Algorithm Adjusts parameters in a module to learn a function from experience / data - (e.g. back-propagation) 7
  • 8. Neural Networks Output layerDirection of Hidden layercalculation Weighted connections Input layerEach node’s value iscomputed as a functionof the weighted sum of itsinputs: 𝑦𝑖 = 𝑓 𝑤 𝑖𝑗 . 𝑥 𝑗 8
  • 9. How to train a neural network (BASIC version)10 Initialise network with some random weights20 Choose a random training example as input30 Compute the output40 Determine error (difference vs. expected output)50 Adjust the weights very slightly to reduce the error60 GOTO 20 9
  • 10. Live Demo – Part 1 10
  • 11. A harder problem…. 11
  • 12. A trick – compression of data 784 outputs decompressor 150 units (“bottleneck”) compressor 784 inputs 12
  • 13. Putting it together 2 10 outputs (one for each digit) 150 units (compressed data)compressor 784 inputs 13
  • 14. Live Demo – Part 2 14
  • 15. Questions? 15