Slides galvin-widjaja

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

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

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

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

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

Expert Systems

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

Build your own image recognition in 20 minutes

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

Define a model: Extract Features
Where are the:
1. Vertical lines
2. Horizonal lines
3. Circles
4. Big circles
5. Clear.. circles?
6. ...squiggles

Define a model: Analyse with DNN
Each feature becomes a set of arbitrary data points that is stored in a large set of
neurons

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.

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

Identify Objects
Basically you draw boxes all over an
image and check if there’s something
interesting in one of them

Suppress bad predictions
Called “intersection over union
(IoU)” This merges good bounding
boxes

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

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

The Limitations for Small timers
and why the limits are being lifted

The Venn diagram of our problems
No Data

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

Lacking Data?
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

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

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

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