These are the slides of my session and ModConf / Liferay DevCon 2016. It attempts to make it easy for any developer to get started with Machine Learning. It presents three exercises which I'm giving as homework (yup, homework, you missed it, right? ;) to the audience. The video for this session is now available at https://www.facebook.com/liferay/videos/vl.383534535315216/10154154247423108/?type=1 (starts at min 34)

1. DEVCON | MODCONF 2016
Machine Learning
The next revolution or
just another hype?
Jorge Ferrer - Vice President, Engineering

2. What new technologies will have a large
impact in the future?
Blockchain Driverless cars
Nanotechnology
Genetic editing
Microservices
Containers
Serverless
IoT
Machine Learning
Is it just hype?

3. Getting started with a new technology
is always hard
3
Libraries
Termin
ology
Setup
Too
many
options
Math
Lang
Platform
Time
IDE
Don’t worry!
We’ve brought a
ladder

4. The slides are already available!
4
tinyurl.com/liferaymlDownload from the App

5. What is Machine Learning?

6. Technology that allows computers to
solve problems that up until now
only humans could solve

7. Where was this picture taken?
Is there a bird in it?

8. How would you tell apart
… an orange … … from an apple?

9. What if you cannot rely on colors?
.

10. Expert Systems, rule-based, have
provided the best results so far…
But often failed in complex problems

11. Identify apples and oranges in context

12. Machine Learning
is nowadays achieving amazing
results in very complex problems

13. Neural Nets,
and Deep Learning in particular, is
providing jaw-dropping ______

14. Neural Nets,
and Deep Learning in particular, is
providing jaw-dropping results

15. Automatic generation of Hemingway
texts
He went over to the gate of the café.
It was like a country bed.
“Do you know it’s been me.”
“Damned us,” Bill said.
“I was dangerous,” I said. “You were
she did it and think I would a fine
cape you,” I said.
“I can’t look strange in the cab.”
“You know I was this is though,” Brett
said.
“It’s a fights no matter?”
“It makes to do it.”
“You make it?”
“Sit down,” I said. “I wish I wasn’t do
a little with the man.”
“You found it.”
“I don’t know.”
“You see, I’m sorry of chatches,” Bill
said. “You think it’s a friend off back
and make you really drunk.”
https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.4lmkisdx8

16. How do Neural Networks work?

17. Artificial Intelligence
Machine Learning
Neural Nets
Deep Learning

18. A simple neural net Activation function
f(sum(x*w))
Input layer Hidden layer Output layer
X1
X2
X3
X4
W1
W1
W2
W1
W2
W1
W2
W1

19. Calculate the price of a house
Area
# of rooms
Size
Age
Price
300k 200k
(Backpropagation / Gradient Descent)
A bit less

20. Repeat hundredths of thousands of
times with a sample set of houses
whose price is known
Hours, Days, Weeks!, Months!!

21. Applying the trained model
348k
Area
# of rooms
Size
Age
Price

22. Deep Learning
More layers:
• Solves more complex
problems
• Exponential increase in
time to train
Source: www.parallelr.com

23. This is an example of
Supervised Learning
Training is done with labelled input data
(i.e. during training the desired answer is known)

24. Unsupervised Learning
Inferring a function to describe hidden structure from
unlabelled data

25. Defining the network
topology is one key
challenge
Often requires training and testing many
of them
25

26. playground.tensorflow.org (Blog post)

27. EXERCISE
27
1 Try to solve the first dataset with two input variables
2
Consider adding on layer and more
neurons to the hidden layers
Try to solve the spiral dataset with all input variables
X1 and X2
3 Add up to 6 layers with several neurons (more time needed)
Try to solve the spiral dataset with just two input variables

28. Machine Learning
development is
more similar to
training a dog than
it is to how you
currently develop
28

29. How do I get started?

30. Most popular ML technologies
Languages
R
Python
Scala
Java
Lua
Matlab/Octave
Tools & Platforms
Jupyter
Orange
Google Cloud Engine
Amazon ML
Azure ML
Pulsar Modules
Libraries
Spark MLlib
Theano
Caffe
Torch
Tensor Flow
Scikit-learn
Weka

31. Languages
R
Python
Scala
Java
Lua
Matlab/Octave
Tools & Platforms
Orange
Jupyter
Google Cloud Engine
Amazon ML
Azure ML
Libraries
Spark MLlib
Theano
Caffe
Torch
Tensor Flow
Scikit-learn
My recommendation to start learning
For Neural Nets (Deep & Wide)
Many ML Algorithms (but not Neural Nets)
Mix of IDE and Google Docs
Learn it. It’s worth it.

32. Spark Example

33. http://spark.apache.org/docs/latest/ml-guide.html

34. HOMEWORK ASSIGNMENT #1
34
2
You will have to create an account. Don’t be lazy, it’s worth it.
Download the housesalesprediction sample data from
kaggle.com and upload it to Jupyter
1
Install the Docker image we have prepared for the exercise
docker run -p 8881:8888 mdelapenya/spark-2-ml
Go to http://localhost:8881 to open Jupyter at the running image
3 Try adding print and plotting functions
Execute everything or cell by cell (With Shift+Ctrl+-)
Share your progress or ask questions at DockerHub

35. http://localhost:8881
Thanks RiccardoThis notebook is available at GitHub

36. Tensor Flow Example

37. Let’s find the bird!

38. tensorflow.org

39. http://tflearn.org/samples

40. HOMEWORK ASSIGNMENT #2
40
1
Install the Docker image we have prepared for the exercise
docker run -p 8882:8888 jorgeferrer/tflearn-cifar-10
Go to http://localhost:8882 to open Jupyter at the running image
3 You will start to notice how Neural Nets take a long time to train.
You might need to take the code out of docker and configure the
usage of your NVidia (hopefully you have one)
Tweak the parameters to get better results
2
Pay attention to how the neural network is built layer by layer.
Try using the TFLearn graph API to print the network
Execute everything or cell by cell (With Shift+Ctrl+-)
Share your progress or ask questions at DockerHub

41. http://localhost:8882
This notebook is available at GitHub

42. Transfer Learning
Reuse generic trained models

43. Facial
recognition
1

44. Automatic description of images

45. Platforms

46. cloud.google.com/ml/

47. aws.amazon.com/machine-learning/

48. azure.microsoft.com/services/machine-learning/

49. Conclusions

50. Machine Learning
The next revolution or just
another hype?
Both!

51. Source
www.forbes.com

52. Claims that it solves everything!
Analysts will demand it
My product has it. Does yours?
LET’S ADD MACHINE
LEARNING!
Soon you will hear…

53. How do we protect ourselves from
excessive hype?
(And still get the best parts of it)

54. Read about it. Play with it
54
It’s not so hard to get started. There are no excuses.
But don’t do it just because of the hype.

55. Do the homework!
TensorFlow
Playground
Spark Machine
Learning
TFLearn
Big thanks to Riccardo Ferrari, Eduardo García and Manuel de la Peña

56. Read Machine
Learning Articles
56
I post my recommendations at
https://getpocket.com/@jorge.ferrer

57. Ready to do some Machine Learning?
If you liked it,
please let me
know!
Thanks!
@jorgeferrer
#LRDEVCON