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Expert systems, transfer learning and its impact on
your next big project
Galvin Widjaja
Lauretta.io

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Galvin Widjaja
@galvinw | github.com/galvinw
Graduated: Singapore Management University is Quant Finance
Work:
Management Consulting → Business Process Management → Monte Carlo
Systems → Process Strategy → IT Strategy → Data Science
Transition:
MIT Visiting Scholar → CFO at Pinchfavor Inc. (New York)
Current:
Founder - Lauretta.io (ML Pipeline and Human Automation)
CEO - Bttrigitical AI (Petrochem AI Solutions)
Management Director - Es Teler 77 Singapore
Indonesia Developer Summit 2017
About Me

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Agenda
● What is transfer learning, what are expert systems and are robots going to take over the world
● Build your own image recognition in 20 minutes
● The Limitations for Small timers and why the limits are being lifted
Indonesia Developer Summit 2017

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Indonesia Developer Summit 2017
Transfer Learning
A field focused on storing knowledge
gained while solving one problem and
applying it to a different but related
problem

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Indonesia Developer Summit 2017

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Expert Systems
A computer system that emulates the
decision-making ability of a human
expert

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Expert Systems

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Are machines going to take over the world?
Well, obviously they are...
Indonesia Developer Summit 2017
So you better be on their good side when it happens

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Build your own image recognition in 20 minutes
Indonesia Developer Summit 2017

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Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Initialize
Python with Keras, Tensorflow and Numpy. Essentially this allows you to do
any image recognition task

12.
Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Define a model: Extract Features
Where are the:
1. Vertical lines
2. Horizonal lines
3. Circles
4. Big circles
5. Clear.. circles?
6. ...squiggles

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Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Define a model: Analyse with DNN
Each feature becomes a set of arbitrary data points that is stored in a large set of
neurons

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Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Looks super hard… but only 9 lines of code
Thanks to Keras (High level framework) and Tensorflow (Handles all the
optimization and complicatedness)
This line says, use
32 types of
features.

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Optional: Recognise the object first
Indonesia Developer Summit 2017
This is hard (we won’t touch it)

16.
Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Initialize
Python with Keras, Tensorflow and Numpy. Essentially this allows you to do
any image recognition task

17.
Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Identify Objects
Basically you draw boxes all over an
image and check if there’s something
interesting in one of them

18.
Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Suppress bad predictions
Called “intersection over union
(IoU)” This merges good bounding
boxes

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Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Suppress bad predictions
First, you choose boxes that don’t
have high likelihood of having a
feature and discard it, then you
merge the rest of the boxes using
“intersection over union (IoU)”.
- This merges good bounding boxes

20.
Indonesia Developer Summit 2017All code at : https://github.com/galvinw/deeplearning.ai thanks to Deeplearning.ai
Do Image Recognition of each box
First, you choose boxes that don’t have high likelihood of having a feature
and discard it, then you merge the rest of the boxes using “intersection
over union (IoU)”.
- This merges good bounding boxes

21.
The Limitations for Small timers
and why the limits are being lifted
Indonesia Developer Summit 2017

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The Venn diagram of our problems
No Data

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Complicated
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Not with these
frameworks
Tensorboard and
development is available
instantly with the use of
tensorflow

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Lacking Data?
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Transfer learning is fully proven to work well on all image recognition.
Most frameworks are trained on ImageNet given you a baseline of 1 million images
Also new techniques seem to be able to use up to 1% as much data as previously required

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Processing Power
Indonesia Developer Summit 2017
With smaller data requirements, processing needs
are decreasing. But even so, Google ML provides
fairly cheap GPU usage with many student
concessions

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Conclusion
The robots are coming to be our overlords.
Get a job as one of their technicians now.
(No seriously, get ready for this)
… also we might be recruiting
Indonesia Developer Summit 2017