This is the code for a talk I gave to the Clojure PDX meetup group on Intro to Deep Learning w/ Clojure using ThinkTopic/cortex. The talk examined the question "Given that Python dominates ML/DL; Is there a valid use case for Clojure?" and came to the conclusion is that deep learning is not a silver bullet but a power tool that can - handle much of the feature engineering, - handles complex (non linear) problems - and has many interesting advancements coming quickly Code and other info are available at https://E-String.com/articles/intro-deep-learning-clojure/

1. Intro to Deep Learning w/ Clojure
It’s Difficult to Make Predictions.
Especially About the Future.
@JulioBarros
Consultant
E-String.com
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2. Hypothesis
Given that Python dominates ML/DL;
Is there a valid use case for Clojure?
TL;DR --It depends
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3. I Hope So
...and I'm not the only one.
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4. Hype or Reality?
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5. "It is a renaissance, it is a golden age,"
"MachinelearningandAIisahorizontalenablinglayer.Itwillempowerand
improveeverybusiness,everygovernmentorganization,everyphilanthropy
—basicallythere'snoinstitutionintheworldthatcannotbeimprovedwith
machinelearning."
—Bezos
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6. Every industry can expect to be transformed by Artificial
Intelligence
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7. Image Classification
Justin Johnson,Andrej Karpathy,Li Fei-Fei-Stanford
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8. My dog Rex
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9. Object Detection
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10. Image Captioning
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11. Dense Captioning
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12. Healthcare
Near or better than human level performance.
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13. Business
— Law & Finance
— Text,audio,image,video understanding
— Churn prediction,customer segmentation
— Product recommendations
— Manufacturing,maintenance and control
— Many many more
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14. Artificial Intelligence
Artificial Intelligence (AI)-the study of"intelligent agents".Reasoning,
knowledge representation,planning,robotics,etc.
— Artificial Narrow Intelligence (ANI)
— Artificial General Intelligence (AGI)
— Artificial Superintelligence (ASI)
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15. Machine Learning
Machine Learning (ML)-Programs that learn from the data and make
predictions.
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16. Types of prediction
Regression-continuous values
Classification-discreet values/labels
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17. Types of ML Algorithms
Supervised-trained on labeled data (regression or classification).
Unsupervised-trained on unlabeled data (clustering,segmentation).
Reinforcement-learns based on outcomes/results of actions.
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18. Deep Learning
Deep Learning (DL)-ML/AIusingartificialneuralnetworks(ANNs)
You might be thinking...
What?Isn'tthatjustaneuralnet?
Didn'tweshowthosedon'twork.
Twice.
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19. Third time is a charm
Capabilities of neural nets have changed dramatically due to
advancements in:
— Data
— Algorithms
— Hardware
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20. Neurons: Biologically inspired
1942 McCulloch and Pitts
1957 Rosenblatt
(A (+ b (apply + (map * x w)))
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21. Activation Function
Introduces non linearity
— Historicaly: Identity,Step,Tanh,Sigmoid
— Currently: Rectified linear Unit (relu),Softmax
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22. Universal Approximation Theorem
(1989)
...a feed-forward network with a single
hidden layer containing a finite number of
neurons can approximate continuous
functions...
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23. Deep Neural Nets
A net with more than one hidden layer.
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24. VGG16
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25. GoogleLeNet
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26. Training
1) Initialize weights randomly
2) Make prediction
3) Measure error (loss)
4) Adjust weights in the right direction
5) GOTO 2
Repeat over and over and over again with your training data
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27. Backpropagation
Finding the right direction.
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28. Backpropagation
To know the right direction calculate the
gradient of the loss function with respect
to each weight.Multiply by the error and a
learning rate to get the delta.
Don'tworry.Thelibrariesdoitforyou.
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29. Millions of Knobs Parameters
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30. A Simple Neural Net in Cortex
(def network-architecture
[(layers/input 11 1 1 :id :x)
(layers/linear 64)
(layers/relu)
(layers/linear 1 :id :y)])
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31. Wine Quality Data
11 features,1 target column
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32. UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets/Wine+Quality
Source: Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez A.Cerdeira,
F.Almeida, T.Matos and J.Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), Porto, Portugal
@2009
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33. Normalized / Scaled data (Standardized)
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34. Demo
https://github.com/thinktopic/cortex
https://github.com/JulioBarros/clj-dl-demo
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35. How Do We Work With Images
Well,images are just numbers/data.
Though numbers close to each other are more related.
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36. Convolutional Layers
Similar to correlations from signal
processing or filters from photoshop.
A small NxN filter is slid over and
convolved/correlated with the image.
Learns to find features.
Then lower level features are combined
into higher level features.
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37. Common Types of Layers
— input / output
— fully connected (dense)
— activation-relu,softmax
— convolutional
— maxpool
— flatten
— drop out
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38. Types of ANN
1. Dense Neural Net (DNN)
2. Convolutional Neural Net (CNN)
3. Recurrent Neural Net (RNN)
4. Everything else
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39. Programming Abstractions
Level Python CLJ/JVM
DSL/API Keras,Lasagne,TF-Slim Cortex,???
Computation Graph,
Backprop,Autograd
Tensorflow,Theano,
Torch,Pytorch,Caffe,
MXNet,CNTK
Cortex,dl4j,Java
bindings
Matrix Math CUDA (cuDNN),Eigen3 Neandertal,
core.matrix,vectorz-clj
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40. Production Considerations
— Target hardware environment
— GPU(s)
— Powerful multicore server
— Mobile device
— Embedded
— Desired latency / scalability
— Data pipeline
— Software engineering practices
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41. Don't Underestimate the Last Two
We need to consider running and maintaining the models in production.
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42. Approaches
— Python based environment
— Clojure based environment
— Generate C++ binaries
— API calls to third party
— API calls to microservices
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43. Challenges with DL
— Needs lots of data.Labeled data is expensive.
— Lacks explain-ability
— Performance requirements-training and inference
— Max performance unclear
— Best architecture unclear
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44. Benefits of DL
— Handles much of the feature engineering
— Handles complex (non linear) problems
— Advancements coming quickly
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45. Recommendations
Do not be intimidated by ANNs or the math.
Start with Keras (and Tensorflow or Theano) tutorials (or maybe Pytorch).
Later choose language/framework as needs dictate.
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46. Resources
Andrew Ng's Coursera Course and Fast.AI Mooc
Deep Learning Book-Goodfellow,Bengio and Courville
Meetups
-Portland-Data-Science-Group
-Portland-Machine-Learning-Meetup
-Portland-Deep-Learning
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47. Thank you! Questions?
Julio@E-String.com
@JulioBarros
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