The term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really? In this session we'll answer that question, providing an approachable overview of machine learning concepts, technologies, and use cases. We'll then take a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning. We'll also survey various machine learning APIs and platforms. Technologies including Spring and Cloud Foundry will be leveraged in the demos. You'll be the hit of your next party when you're able to express the near-magical inner-workings of artificial neural networks!

1. Machine Learning Exposed!
James Weaver
Developer Advocate
@JavaFXpert
#s1p #springone

2. From introductory video in Machine
Learning course (Stanford University
& Coursera) taught by Andrew Ng.
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3. Self-driving cars
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4. Generating image
descriptions
Credit: Andrej Karpathy, Li Fei-Fei
http://cs.stanford.edu/people/karpathy/deepimagesent/
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5. Supervised Learning
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6. Supervised learning regression problem
(from Andrew Ng’s Machine Learning course)
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7. Unsupervised Learning
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8. Unsupervised learning finds structure in unlabeled data
(e.g. market segment discovery,
and social network analysis)
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9. Reinforcement
Learning
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10. AlphaGo is a recent reinforcement learning success story
Source: https://gogameguru.com/i/2016/03/AlphaGo-Lee-Sedol-game-3-game-over.jpg@JavaFXpert

11. Supervised learning classification problem
(using the Iris flower data set)
Sepal
length
Sepal
width
Petal
length
Petal
width
Species
5.1 3.5 1.4 0.2 Iris setosa
4.9 3.0 1.4 0.2 Iris setosa
7.0 3.2 4.7 1.4 Iris versicolor
6.4 3.2 4.5 1.5 Iris versicolor
6.3 3.3 6.0 2.5 Iris virginica
5.8 3.3 6.0 2.5 Iris virginica
Features Labels
Training / test data
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12. Iris data classified
in four dimensions
Sepal
length
Sepal
width
Petal
length
Petal
width
Species
5.1 3.5 1.4 0.2 Iris setosa
4.9 3.0 1.4 0.2 Iris setosa
7.0 3.2 4.7 1.4 Iris versicolor
6.4 3.2 4.5 1.5 Iris versicolor
6.3 3.3 6.0 2.5 Iris virginica
5.8 3.3 6.0 2.5 Iris virginica
Credit: Tal Galili
https://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html
Features Labels
Training / test data
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13. Modeling the brain works well with machine learning
(ya think?)
(inputs)
(output)
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14. Anatomy of an Artificial Neural Network
(aka Deep Belief Network when multiple hidden layers)
Input layer Output layerHidden layers
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15. Neural net visualization app (uses Spring and DL4J)
https://github.com/JavaFXpert/visual-neural-net-server
https://github.com/JavaFXpert/ng2-spring-websocket-client
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16. Entering feature values for prediction (classification)
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17. Visual Neural Network application architecture
Spring makes REST services and WebSockets easy as π
Neural Net
Model Listener
HTML5 client
(angular 2 & visjs)
DeepLearning4j
library
Neural net graph
(WebSocket)
Prediction
REST service
prediction &
activations
Model Selection
Handler
connect &
subscribe
(WebSocket)
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18. @JavaFXpert
The app leverages machine learning library found at deeplearning4j.org

19. Simple neural network trained for XOR logic
forward propagation
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20. Feedforward calculations with XOR example
For each layer:
(1 x 8.54) + (0 x 8.55) = 8.54
1 / (1 + e -4.55 ) = 0.99Use sigmoid activation function:
Multiply inputs by weights:
Add bias: 8.54 + (-3.99) = 4.55
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21. Simple neural network trained for XOR logic
back propagation (minimize cost function)
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22. Back propagation
Uses gradient descent to iteratively minimize the cost function
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23. @JavaFXpert
kaggle.com is a great website for data science and machine learning enthusiasts

24. @JavaFXpert
Let’s use a dataset from kaggle.com to train a neural net on speed dating

25. Identify features and label we’ll use in the model
Let’s use 65% of the 8378 rows for training and 35% for testing
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26. Code that configures our speed dating neural net
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27. Trying our new speed dating neural net example
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28. Making predictions with our speed dating neural net
Note that input layer neuron values are normalized
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29. Machine Learning Exposed!
James Weaver
Developer Advocate
@JavaFXpert
#s1p #springone
(Thanks for your kind attention)