1. Machine Learning for Software Developers in 45 Minutes
Why, How and Whoa!
Room 2016
Session DEV5090 — MONDAY Oct 22, :30pm - 2:15am PT

2. Speakers Dr Zoran Sevarac @zsevarac Frank Greco @frankgreco
AI Researcher Univ of Belgrade, Serbia JC, Groundbreaker Ambassador,
NetBeans Contributor
Frank
Chair NYJavaSIG - NY Java User Group JC, Cloud/Mobile Architect

3. Goal
To explain what Machine Learning is, why it’s important and how Java Developers can create their own ML apps

4. Session Topics Why Do We Need Machine Learning
What is Machine Learning
Brief History
Basic Terminology
How to Get Started for Java Devs and Demos
In Big Data, “Quants” (math/eng experts) are needed. These humans can create models from the data, typically a few per week.
Machine can automate the correlations, pattern match and model building significantly faster using ML techniques. ML can create thousands of models per week.

5. Session Topics Why Do We Need Machine Learning
What is Machine Learning
Brief History
Basic Terminology
How to Get Started for Java Devs and Demos
In Big Data, “Quants” (math/eng experts) are needed. These humans can create models from the data, typically a few per week.
Machine can automate the correlations, pattern match and model building significantly faster using ML techniques. ML can create thousands of models per week.

6. For many applications, the algorithms are well-defined

7. Total money made from these turnstiles for the past week...
Calculate bowling avgs
Find highest score
Sort all the scores, etc.
Total money made from these turnstiles for the past week...
Count the number of popular repos written in python

8. Traditional Software Traditional Software T F Traditional Software
Typically a collection of if-then rules
Algorithms are essentially designed as if a human (or team) is doing the work... just faster, more distributed or more scalable
Expert systems which are rules engines, just make this easier or more convenient.

9. What Problem does ML Solve?
Chess has simple rules.
64 Squares
16 Attackers/Defenders per side
Well-defined moves
Rules are easy to describe.
Straightforward to program.
But of course… how to win is a different but related thread...
Easy to program. But hard to win consistently even if you know the rules.

10. But some algorithms are NOT well-defined…
Let’s look at Visual Recognition

11. Chihuahua or Muffin?

12. Karen Zack @teenybiscuit
Chihuahua or Muffin?
Visual Recognition (an ML example)
Requires deeper thought
Potentially subjective
Rules are very hard to describe.
Huge number of rules increases the complexity.
Millions of lines of code… hard to program… worse: hard to debug
Hard to get correct.
Early AI/ML systems failed partially due to “hard-coded” rules.
Karen Zack @teenybiscuit

13. Use Cases for Visual Recognition alone…
Ensure positioning of assembly products
Travel and retail suggestions
VisRec JSR #381 (pending) - Visual Recognition for Java
Visual compliance or deterioration
Track logos on social media

14. Handwriting translation
Many of These Applications are Hard to Code
Many applications are not explicitly programmable…
Machines can automate the correlations and model building...
Face recognition
Handwriting translation
Autonomous Driving
According to MIT Tech Review, the most popular ML use cases to adopt:
Visual Recognition, Classification
Sentiment Analysis
Text Mining
Natural Language Processing

15. So how do we solve these types of problems?

16. Session Topics Why Do We Need Machine Learning
What is Machine Learning
Brief History
Basic Terminology
How to Get Started for Java Devs and Demos

17. Artificial Intelligence
Machine Learning
A type of AI that allows a machine to learn from experience/data
Deep Learning
A type of ML that uses powerful computing resources and advanced neural networks to more-accurately solve non-linear, highly-dimensional problems with large amounts of data (eg, vis rec)
Artificial Intelligence
A type of algorithm(s) that allows a machine to emulate aspects of intelligent human behavior
AI was a “boil the ocean” idea in the 1950’s. ML (1980+) and DL (2010+) are collections of “heat up large vats of soup” ideas. The latest innovations are occurring in DL… more accuracy… more computation… more sophisticated neural net architectures.

18. In ML, the machine is modeling the solution, not the human.
The machine looks at example data, detects patterns and correlations and predicts outcomes
Q: How does it determine correlations? ANS: It uses statistical regression techniques.
Stats building the model for specific data
ML building the model of generalization (it could be wrong)

19. https://towardsdatascience
Note: Only the two on the left side are clustering, other three are classification algorithms

20. ML is already heavily used
Recommendations
Fraud detection
Spell checkers
Targeted ads
Customer support
Smart oil rig
Gmail anti-spam
Other targets… how about an ML-infused IDE? Like a super spell-checker… “Excuse me Dave, but I don’t think you meant to use that service. You probably meant foo…”

21. Who Are the Big Players with Engines/Services?
And dozens of startups...

22. Session Topics Why Do We Need Machine Learning
What is Machine Learning
Brief History
Basic Terminology
How to Get Started for Java Devs and Demos
In Big Data, “Quants” (math/eng experts) are needed. These humans can create models from the data, typically a few per week.
Machine can automate the correlations, pattern match and model building significantly faster using ML techniques. ML can create thousands of models per week.

23. Quiz When was the first “AI” conference? 1972? 1985? 1955? 1999? 2001?
But Didn’t We Try This Already?
Yes… But AI had overly broad scope
“Intelligent agents, first-order logic, knowledge ontologies, probabilistic reasoning, learning theory, NLP, robotics,…”
Quiz When was the first “AI” conference?
1972? 1985? 1955? 1999? 2001?

24. Punch cards weren’t even invented in 1955!

25. First Artificial Neural Network
1943
Warren McCulloch
Walter Pitts
First subroutine… 1946!

26. Computing resources were not yet capable Not enough data to analyze
So… What Happened
Computing resources were not yet capable
Not enough data to analyze
Early ML attempts did not mimic the brain well
Boil the Ocean - too big of a problem
Cool research but funding was erratic due to lofty goals and missed milestones.

27. But Now... Scalable Cloud Computing Large Collections of Data
Improved Neural Networks
Cloud resources plus huge data sets
… billions/trillions of data points
… hundreds/thousands of machines in the cloud
… better ML algorithms

28. Software vs. Humans
Jeopardy 2011
Chess 1997
Go 2016

29. At least 15x-30x faster than GPU/CPUs for ML applications
New Approaches to Hardware Addresses Dark Silicon
At least 15x-30x faster than GPU/CPUs for ML applications
One can envision an interesting ML-infused future with Project Panama and JShell (or another JVM language or DSL).
Software architects now need to consider non-core-based scalable solutions.
Back in the day, we only focused on “cores”..

30. https://www. bloomberg

31. Session Topics Why Do We Need Machine Learning
What is Machine Learning
Brief History
Basic Terminology
Useful Tools for Java Devs
Cool Demos!
In Big Data, “Quants” (math/eng experts) are needed. These humans can create models from the data, typically a few per week.
Machine can automate the correlations, pattern match and model building significantly faster using ML techniques. ML can create thousands of models per week.

32. Basic Terminology
Machine learning gives "computers the ability to learn from data without being explicitly programmed.”
Data set - set of data that represent specific problem we’re trying to solve
Model - A data structure that consists of various parameters and methods that transform inputs into outputs. Includes some kind of training procedure.
Training/learning procedure, adjusts internal model parameters based on the data set in order to solve specific problem (learn data set)
Performance measure - metric that is used to tell how good model has learned the data.
2 min

33. Input Data algorithms Predictions Training Data
The ML algorithms are at the center.
So what type of algorithms are there

34. ML - Basic Workflow Data Prep Train Model Test Model Predict
Analyze Errors
Data Prep
Train Model
Test Model
Predict
Target/goal?
Java developer should be familiar with this basic workflow. Its not comprehensive and you’ll learn more about the subtleties as you progress.
Test Model - using regression, cross-validation
Test data set as well
Data preparation: data scientists, data engineers (Spark, cloud, cluster, etc)
Prepare/clean data (filter outliers, resolve missing values, normalize, etc.)
Build model using training data
Test model using test data
Hints
What is the specific business question that you’re asking?
What is the expected result of applying machine learning?
What type of problem is that (regression, classification, etc)?

35. Types of Machine Learning
Supervised
Unsupervised
Reinforcement

36. Types of Machine Learning
Supervised
Learns from examples that provide inputs and desired outputs. “Teacher” supervises correction.
Unsupervised
Reinforcement
Type of machine learning algorithms
Learns from examples that provide inputs and target/desired outputs
It can learn input-output mapping
Learning is based on iteratively feeding inputs into model, and minimising the error between the actual and target output of the model (error/loss function)

37. Session Topics Hand off to Zoran Why Do We Need Machine Learning
What is Machine Learning
Brief History
Basic Terminology
How to Get Started for Java Devs and Demos
Hand off to Zoran

38. Popular Java Toolkits (small subset)
VisRec JSR #381
VisRec JSR 381

39. What you can do with ML?
Classification - assign a predefined label/category to an item, enum output Example: spam/not spam, fraud/not fraud, object on image is {cat, dog, rabbit}
Regression - model the relationship between variables, real valued output Example: Server load depending of the number of users/requests
Clustering (identify and assign input samples to groups) Example: What are the typical behaviours of users on my website?
Important question What type of problem is that?
1 min
Fisrt step:
Know your question
Determine the type of ml task
And set target performance value, desired accuracy

40. Introductory Algorithms
Linear Regression (regression)
Logistic Regression (classification)
Neural Networks & Deep Learning (classification & regression)
All use the same general
Supervised learning algorithm
The evolution of deep learning
Why al ml courser start with linear regression
For each algorithm I’ll briefly explain
What it does
General principle how it does it
Essential things to understand inoreder to be able to apply it and tweak it for specific problem
What paramaters it has and how they influence the performance / how well algorithm perfroms

41. General Supervised Learning
while(error > errorThreshold) { predictedOutput = model.getOutput() error = actualOutput - targetOutput moveCloserToErrorMinimum() }
Iterative error minimization algorithm (aka optimization algorithm)
Least Mean Squares, Gradient Descent

42. Linear Regression
What it does: Finds the best possible straight line that through given set of data points. A rough estimate of a global trend.
Assumes near-linear dependency/trend between variables: y = slope * x + intercept
How it works: finds the set of parameters (slope and intercept) that gives minimum of the error function. Error is calculated as difference between actual and predicted value.
Two important concepts: Error/Loss Function Optimization method (gradient descent - an iterative procedure for error function minimization) .
Demo:
Apache few numbers
Weka Code house price prediction
No deep netts during presentation, but include link on gith hub examples
Explain the problem, data set
Show basic essential code in slide (example )
Link to full example on github (sevarac) & blog (deep netts and my)
Weka, Deep Netts, Vis Rec Example
Link to blog post deepnetts/blog/Linear_Regression.html (samo napravi link I stavi nek pocetni sadrzaj. Vremenom i stavi link na github primer. Idealno kratak you tube , i osnovne formule. Objasnjenja za Java programere)
Na slajdu objasni sta radi, a kroz primere kako se koristi: What It Does and How To Use
Linear means uniform
For best results: remove outliers and correlated inputs, use inputs that are correlated with output

43. Logistic Regression/Binary Classification
Just put Linear Regression inside Logistic function
Fits logistic function: y=1/(1+e^-x)) to given data
Used for binary (yes/no) classification problems
Demo: breast cancer prediction using weka
Yes/No classificatin or with fancy name Binary Classification

44. Neural network
Feed Forward Neural Network - a directed graph in which each unit performs logistic regression.
Learns using Back Propagation algorithm which is also an error function minimization.
Can be used for both Classification and Regression problems
Number of nodes and layers
Activation Function (Logistic, Tanh, ReLU)
Error Function (Mean Squared Error, Cross Entropy)
Demo: Iris Classifier using Deep Netts
Multi Class Nonlinear classification

45. Convolutional Network / Deep Learning
Extension of Feed Forward Neural Network specialized for image classification/recognition taska
Introduces convolutional layers (2D and 3D) which act as a learnable image filters - feature extractors
Reduces amount or image preprocessing - preprocessing is being learned
Demo: Mnist using Deep netts

46. VisRec API JSR 381
To simplify and standardize Java APIs for classifying, and recognizing objects in images.
VisRec JSR #381

47. VisRec API and the future of Machine Learning For Java
Standard Java API for visual recognition based on machine learning - like a Collections API for machine learning
Reusable design for machine learning tasks (interfaces, abstract classes, builders)
General Machine Learning Layer (data set, classification, regression )
Visual Recognition Task Layer (image classification, object recognition)
Easy integration & interoperability with other Java Technologies and ML frameworks
Demo: VisRecMnistExample

48. Learn and Contribute to the Next Gen Java ML API
Official Page for VisRec JSR #381 at JCP -
Github Repo -
Wiki -
Mailing list -
VisRec JSR - An architecture for Visual recognition using machine learning, make it easy to integrate - combine with previous slide

49. Oracle Data Science Cloud
COMING SOON
Support for all open source ML libraries
Support for Team Collaboration
Workflow support for entire lifecycle from data analysis and model building to monitoring
Visit them at the booth!

50. Experts in Modern Development
Cloud
Microservices and Containers
Java, JavaScript/Node.js, PHP, Python
DevOps
Continuous Delivery
Open Source Technologies
SQL/NoSQL Databases
Machine Learning, AI, Chatbots
developer.oracle.com/ambassador
@groundbreakers

51. Questions? Thank you! @frankgreco @zsevarac Continue Learning at
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