machine learning the next revolution or just another hype
TRANSCRIPT
DEVCON | MODCONF 2016
Machine Learning The next revolution or just another hype? Jorge Ferrer - Vice President, Engineering
What new technologies will have a large impact in the future?
Blockchain Driverless cars
Nanotechnology
Genetic editing
Microservices Containers Serverless
IoT
Machine LearningIs it just hype?
Getting started with a new technology is always hard
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Libraries
Terminology
Setup
Toomanyoptions
Math
Lang
Platform
Time
IDE
Don’t worry!We’ve brought a ladder
The slides are already available!
4tinyurl.com/liferaymlDownload from the App
What is Machine Learning?
Technology that allows computers to solve problems that up until now
only humans could solve
Where was this picture taken?
Is there a bird in it?
How would you tell apart
… an orange … … from an apple?
What if you cannot rely on colors?.
Expert Systems, rule-based, have provided the best results so far…
But often failed in complex problems
Identify apples and oranges in context
Machine Learningis nowadays achieving amazing results in very complex problems
Neural Nets, and Deep Learning in particular, is
providing jaw-dropping ______
Neural Nets, and Deep Learning in particular, is
providing jaw-dropping results
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
How do Neural Networks work?
Artificial Intelligence
Machine Learning
Neural Nets
Deep Learning
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
Calculate the price of a house
Area
# of rooms
Size
Age
Price300k 200k
(Backpropagation / Gradient Descent)
A bit less
Repeat hundredths of thousands of times with a sample set of houses
whose price is known
Hours, Days, Weeks!, Months!!
Applying the trained model
348k
Area
# of rooms
Size
Age
Price
Deep LearningMore layers:
• Solves more complex problems
• Exponential increase in time to train
Source: www.parallelr.com
This is an example of Supervised Learning
Training is done with labelled input data (i.e. during training the desired answer is known)
Unsupervised LearningInferring a function to describe hidden structure from
unlabelled data
Defining the network topology is one key challengeOften requires training and testing many of them
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playground.tensorflow.org (Blog post)
EXERCISE
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1 Try to solve the first dataset with two input variables
2Consider adding on layer and more neurons to the hidden layers
Try to solve the spiral dataset with all input variables
X1 and X2
3Add up to 6 layers with several neurons (more time needed)Try to solve the spiral dataset with just two input variables
Machine Learning development is more similar to training a dog than it is to how you currently develop
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How do I get started?
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
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
Weka
My recommendation to start learning
ForNeuralNets(Deep&Wide)
ManyMLAlgorithms(butnotNeuralNets)
MixofIDEandGoogleDocs
Learnit.It’sworthit.
Spark Example
HOMEWORK ASSIGNMENT #1
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2You 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 exercisedocker run -p 8881:8888 mdelapenya/spark-2-ml Go to http://localhost:8881 to open Jupyter at the running image
3Try adding print and plotting functionsExecute everything or cell by cell (With Shift+Ctrl+-)
Share your progress or ask questions at DockerHub
http://localhost:8881
Thanks RiccardoThisnotebookisavailableatGitHub
Tensor Flow Example
Let’s find the bird!
HOMEWORK ASSIGNMENT #2
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1 Install the Docker image we have prepared for the exercisedocker 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
2Pay 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
http://localhost:8882
ThisnotebookisavailableatGitHub
Transfer Learning Reuse generic trained models
Facial recognition
1
Automatic description of images
Platforms
azure.microsoft.com/services/machine-learning/
Conclusions
Machine Learning The next revolution or just another hype?
Both!
Source www.forbes.com
Claims that it solves everything!
Analysts will demand it
My product has it. Does yours?
LET’S ADD MACHINE LEARNING!
Soon you will hear…
How do we protect ourselves from excessive hype?
(And still get the best parts of it)
Read about it. Play with it
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It’s not so hard to get started. There are no excuses.But don’t do it just because of the hype.
Do the homework!
TensorFlow Playground
Spark Machine Learning
TFLearn
Big thanks to Riccardo Ferrari, Eduardo García and Manuel de la Peña
Read Machine Learning Articles
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I post my recommendations at https://getpocket.com/@jorge.ferrer
Ready to do some Machine Learning?
If you liked it, please let me
know!
Thanks!
@jorgeferrer #LRDEVCON