machine learning live
TRANSCRIPT
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Machine Learning – A definition
"Field of study that gives computers
the ability to learn without being
explicitly programmed.“
Arthur Samuel, 1959
Source: Good Old Wikipedia 2
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Learning = Building functions from experience
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Task Input Output
Simple mathematical
function
x y = sin(x)
Spam filtering Text of an email
message
Probability of email being
spam (%)
Stockmarket
prediction
Historical data on
- Stock prices
- Economic indicators
Expected price
movements
Remembering
names
Thought:
“Who was that guy
who liked windsurfing?
Thought:
“Oh yes – that was Bob”
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The state of machine learning
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“It works! sort of…. sometimes…. on a good day….”
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nuroko.com
We’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
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Why Clojure?
Productivity and fun!
Good parts of the JVM
Interactive experiments
Functional programming
DSLs with composable abstractions
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REPL
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Some Key Abstractions
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Module
Algorithm
Coder
Vector 1 0 1 1 0 Efficiently represents information as a
vector of double values
Converts arbitrary data into vectors (and
back again!)
Represents a function
- (e.g. a Neural Network)
Adjusts parameters in a module to learn a
function from experience / data
- (e.g. back-propagation)
Task Represents a problem to solve – typically
via provision of training examples
𝑜𝑢𝑡𝑝𝑢𝑡 = 𝑓 𝑖𝑛𝑝𝑢𝑡
1 0 1 1 0 “Cat”
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Neural Networks
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Output layer
Input layer
Hidden layer
Weighted connections
Direction of
calculation
Each node’s value is
computed as a function
of the weighted sum of its
inputs:
𝑦𝑖 = 𝑓 𝑤𝑖𝑗 . 𝑥𝑗
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How to train a neural network
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(BASIC version)
10 Initialise network with some random weights
20 Choose a random training example as input
30 Compute the output
40 Determine error (difference vs. expected output)
50 Adjust the weights very slightly to reduce the error
60 GOTO 20
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Live Demo – Part 1
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A harder problem….
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A trick – compression of data
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784 inputs
784 outputs
150 units (“bottleneck”)
compressor
decompressor
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Putting it together
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compressor
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784 inputs
150 units (compressed data)
10 outputs (one for each digit)
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Live Demo – Part 2
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Questions?
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