Presentation given to PHP developer meetup group.

1. 10 THINGS
EVERY PHP DEVELOPER
SHOULD KNOW ABOUT
MACHINE LEARNING

2. Fill in the gaps and squash hype around M.L.
Build the case for using it now.
And provide easy ways to get started.
TODAY’S GOALS

3. Background
#1: What it is
#2: It’s taking over
#3: How it works
#4: Different approaches
#5: Where it’s used
#6: How to get started
OUR JOURNEY
Code
#7: Recommendations
#8: Content analysis
#9: Computer speech
#10: Computer vision

4. INTRODUCTIONS

5. ABOUT ME
chris.mohritz@10xeffect.com
● Lifelong entrepreneur
● Deep technology background (strategy, not developer)
● Using A.I. (machine learning) in business since 2009
● Opening a startup accelerator in Vegas

6. HOW I GOT STARTED
Apache
Mahout
(Decision Forest)
Behavior
prediction
Suite of
mobile apps
Determine most relevant (highest-converting)
sales offer to present to each individual user —
and the best (highest-converting) time to
present it.
circa 2009

7. Will the current user buy “Madden NFL” right now?
WHAT IS A DECISION FOREST?
is male?
is age
> 16?
is Y app
installed?
is X app
installed?
end
has used >
30 days?
was X
function
used?
was Y
function
used?
no
yes
no
yes
no
yes
no
yes
end
(better ways to do this now)
no
yes
end
do it

8. #1: WHAT IS IT?

9. “Field of study that
gives computers the
ability to learn without
being explicitly
programmed.”
~ Arthur Samuel, 1959
MACHINE LEARNING IS...
analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling

10. Training your computer to do stuff,
just like you would train a pet.
IN OTHER WORDS...

11. SIMILAR TO HOW WE LEARN
Data System Output
Model
Question Answer
Emotions
Mindset
Algorithm
The reference data pattern
(decision-making stuff)
Process the computer uses
to ‘learn’ the model
The model is built from
historical data Training data
Life experience
Perspective
Algoritm

12. AT THE END OF THE DAY...
It’s all pattern recognition.

13. #2: WHY SHOULD I CARE?

14. SOFTWARE IS EATING THE
WORLD
Everything is becoming code.
Software automates, simplifies, and
accelerates business.

15. BUT...
Someone needs to write the code/logic.

16. THE TRADITIONAL WAY
Handwritten logic.
If / Then / Else

17. THE PROBLEM
Complex situations require
complex2
software logic.

18. THE SOLUTION
Trained logic using historical data.
Model
tripID hasEggs eggsBough
t
milkBought
1 1 6 1
2 0 0 2
3 1 6 1
4 1 8 1
5 0 0 1
6 1 6 1
7 0 0 3
0011001101011101
0101100111010
11001001101
Stored as a
mathematical
model.
Finds patterns in
the data.

19. WHAT IT LOOKS LIKE
console.aws.amazon.com/machinelearning/home?region=us-east-1#/datasources

20. M.L. IS EATING THE SOFTWARE
All applications are becoming “smart” — with
unprecedented complexity in logic.
Machine learning automates, simplifies, and
accelerates software.

21. ENDLESS OPPORTUNITIES
Everything mankind has ever invented — including all
software apps — will be reinvented using A.I.

22. A QUICK NOTE...
In the near future, A.I. be writing it’s own code.

23. #3: HOW DOES IT WORK?

24. STILL A MURKY LANDSCAPE
Artificial Intelligence
Machine Understanding (?)
Pattern recognition
Classification
Prediction
Can only do one thing
Brute-force approach
Autonomous decisions
Universally applicable
Intuition approach
Google DeepMind
Amazon Machine Learning
Natural language processing
Computer vision
Optimization
IBM Watson
Classic learning
Multi-tiered
deep learning
neural networks
Deep learning
neural network
Explicit ProgrammingHandwritten
Machine Learning
logiccomplexity

25. IT’S ALL CLASSIFICATION
via: wjscheirer.com

26. “Features”
Points of differentiation within the data.
How would you teach a
child to recognize the
differences?
● Distance between eyes
● Width of nose
● Shape of cheekbones
● etc.
HOW DOES IT CLASSIFY?

27. “Probability”
Each potential
answer gets a
numeric
probability
calculated for it.
Higher
probability
means greater
confidence.
HOW DOES IT MAKE DECISIONS?

28. ● Supervised learning — Labeled training data
● Unsupervised learning — Unlabeled training data
● Transfer learning — Applying aspects across
models
● Reinforcement learning — Reward-based training
TRAINING
gym.openai.com

29. #4: WHAT ARE THE DIFFERENT
APPROACHES?

30. REMEMBER...
Data System Output
Model
Question Answer
Emotions
Mindset
Algorithm
The reference data pattern
(decision-making stuff)
Process the computer uses
to ‘learn’ the model
The model is built from
historical data Training data
Life experience
Perspective
Algoritm

31. Who wants to be a
data scientist?
ENDLESS ALGORITHMS
docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice
machinelearningmastery.com/a-tour-of-machine-learning-algorithms

32. No “hidden”
layers
“CLASSIC” LEARNING
playground.tensorflow.org

33. 1-2 “hidden”
layers
“SHALLOW” LEARNING
playground.tensorflow.org

34. >2 “hidden”
layers
“DEEP” LEARNING
playground.tensorflow.org

35. (SIMPLE) NEURAL NETWORK
Each layer performs a
discrete function
≥ 1 input
neurons
≥ 1 output
neurons
≥ 1 hidden layers
Output “fires” if all
weighted inputs sum
to a set “threshold”
Each connection applies a
“weighted” influence on
the receiving neuron
Layers build on each other
(iterative)
Each input can
be a separate
“feature”
Each neuron takes in
multiple inputs
Hidden layers can’t directly
“see” or act on outside world
cs231n.github.io/neural-networks-1

36. HOW MUCH IS A HOUSE WORTH?
Decisions based on combinations.
3 bedrooms
37 years old
1450 ft2
$191,172
Is it “old” or “historic?”
Is it “small” or “open floor plan?”
$32,108 per bedroom
$64,251 per acre
Need a lower weight for “old”
Apply initial
abstractions
Set values
cs231n.github.io/neural-networks-1

37. #5: WHERE IS IT USED?

38. ENDLESS USES
● Classifying DNA sequences
● Economics
● Fraud detection
● Medical diagnosis
● Search engines
● Speech recognition
● Job search
● Spam filtering
● Risk prediction
● Visual product search
● Create art / music
● Industrial design
● Image caption generation
● Facial recognition
● Colorization of b&w images
● Adding sound to silent movies
● Language translation
● Image editing
● Vehicle navigation
● Error detection

39. IN THE WILD
Recommender
(pick from list)
Classifier
(binary)
Visual
recognition
(deep learning)

40. #6: WHERE SHOULD I START?

41. ● You don’t need a supercomputer
● You don’t need to write a ton of code
● You don’t need to invest massive amounts of time
● You don’t need a data science degree
● You don’t need to be a math whiz
● You don’t need mountains of data
MYTH BUSTING

42. IT’S EASIER THAN YOU THINK
Forget theory, just do it.

43. ● Amazon Artificial Intelligence
● Google Cloud Machine Learning
● Microsoft Cognitive Services
● IBM Watson *
● DiffBot
* - PHP library is 3rd-party
SaaS OPTIONS

44. ● TensorFlow *
● Amazon DSSTNE *
● H2O *
● PredictionIO
● Apache Mahout
● Scikit Learn
● Caffe *
OPEN SOURCE OPTIONS
● Microsoft CNTK *
● Torch *
● Theano *
● MXnet *
● Chainer *
● Keras *
● Neon *
* ANN / Deep learning

45. ● archive.ics.uci.edu/ml
● deeplearning.net/datasets
● mldata.org
● grouplens.org/datasets
● cs.toronto.edu/~kriz/cifar.html
● cs.cornell.edu/people/pabo/movie-review-data
● yann.lecun.com/exdb/mnist (handwriting)
● kdnuggets.com/datasets/index.html (long list)
● image-net.org (competition)
OPEN SOURCE DATASETS

46. #7: RECOMMENDATIONS

47. A CUSTOMER-DRIVEN WORLD
Today, consumers control the brand-customer
relationship. They choose when and how they interact.
Brands need to create attractive experiences that
draw consumers in — through highly relevant
communications and products.

48. PRODUCTS

49. CONTENT

50. ENDLESS APPLICATIONS
● Visitors who viewed this product also viewed
● Visitors who viewed this product ultimately bought
● You might also like
● Recently viewed
● Trending in category
● Site-wide top sellers
● Customer also bought
● Other customers who bought this product also bought
● Items viewed with items in your cart
● Top sellers from your recent categories on homepage

51. RECOMMENDATIONS API
microsoft.com/cognitive-services/en-us/recommendations-api

52. Recommendations
Build
FBT Build
Model Application
Training
Catalog
Training
Usage
HOW IT WORKS
Related item
recommendations
Recommendations API
Frequently bought together
recommendations

53. PLAN PLAN LIMITS PRICE
Free 10,000 calls / mo Free
S1 Standard 100,000 calls / mo
$75 / mo
(overage at $0.75 / 1000 calls)
S2 Standard 1,000,000 calls / mo
$500 / mo
(overage at $0.75 / 1000 calls)
S3 Standard
10,000,000 calls / mo
(overage at $0.75 per 1K calls)
$2,500 / mo
(overage at $0.75 / 1000 calls)
S4 Standard 50,000,000 calls/mo
$5,000 / mo
(overage at $0.75 / 1000 calls)
PRICING

54. Catalog
Usage
TRAINING DATA

55. Guide: gigaom.com/2017/02/08/building-a-recommendation-engine-using-microsoft-azure
Code: github.com/10xNation/microsoft-recommendation-engine
DEMO

56. #8: CONTENT ANALYSIS

57. IBM WATSON NATURAL LANGUAGE
UNDERSTANDING
ibm.com/watson/developercloud/natural-language-understanding.html
● Code: github.com/10xNation/ibm-watson-natural-language-understanding-php
● GUI: natural-language-understanding-demo.mybluemix.net

58. #9: COMPUTER SPEECH

59. AMAZON POLLY
console.aws.amazon.com/polly/home
Code: github.com/10xNation/amazon-polly-demo-php

60. #10: COMPUTER VISION

61. GOOGLE CLOUD VISION
cloud.google.com/vision
Guide: gigaom.com/2017/02/06/harnessing-visual-data-using-google-cloud
Code:
github.com/GoogleCloudPlatform/php-docs-samples/tree/master/vision/api

62. CLOSING

63. Intro blog posts:
● Artificial Intelligence 101 (the big picture)
● Machine Learning 101 (what you’ll actually use)
New ‘How to Apply A.I. in Your Business’ blog series:
● Voice-Powered Products w/ Amazon Alexa
● Predictive Social Media w/ IBM Watson(live)
● Image Recognition w/ Google Cloud
● Recommendation Engine w/ Microsoft Azure
GO DEEPER

64. THANK YOU
chris.mohritz@10xeffect.com