1.
Overview
● Brief Skymind Intro
● Deep Learning outside research
● Core trends for ROI in deep learning
● Anomaly Detection with deep learning
● Simbox fraud detection for telco
● Network Intrusion
● Fintech securities churn prediction
● Real time corporate campus security: Detecting
dangerous objects

2.
Distributed Deep
RL on Spark
We built
Deeplearning4j

3.
SKYMIND INTELLIGENCE LAYER (SKIL)
REFERENCE ARCHITECTURE

4.
Deep Learning
outside research
● Too much hype
● Most companies rarely do machine learning let
alone deep learning
● Beginners try to jump to deep learning after
andrew ng’s coursera class without first
principles
This is not deep learning.
This is deep
learning.

5.
Deep Learning
outside research
● Mostly python and r on kaggle
● Many learning from udacity
● Most deep learning is research stage/enthusiast
● Salaried engineers doing DL mostly publishing
papers
● Large fight for talent (see google fellowship)

6.
Deep Learning
outside research
● Deep Learning hasn’t penetrated the fortune
2000
● Fortune 2000 wants ROI not cat pictures
● Many organizations just NOW starting to take
software seriously let alone data science
● Use cases for deep learning still not widely
understood
● Large fight for talent (see google fellowship)

7.
Core trends for
ROI in DL
● Mostly funded by adtech companies
● Companies doing DL have data from lots of
media data (audio,image,video)
● Many companies using DL for ad targeting
● Best use cases are targeting understanding large
scale hidden patterns in data (often cross
domain)
● Time series has largely been ignored

8.
Core trends for
ROI in DL
● Initial first attempts at deep learning following
papers (no other examples)
● Many companies end up sticking to simpler
techniques after trying DL
● Expectations for DL tend to match hype not
reality
● Some rare cases exist outside this trend (mainly
in asia)

9.
Core trends for
ROI in DL
For more trends
see:
https://www.oreilly.c
om/ideas/the-curren
t-state-of-machine-i
ntelligence-3-0

10.
Anomaly
Detection
● “Find the needle in the haystack”
● “Find the bad guy”
● “The machines about to break!”
● “Find the next market rally”
● “Take action on said anomaly”

11.
Anomaly
Detection with
deep learning
● Both unsupervised and supervised techniques
● LSTMs (time series neural net)
● Autoencoders (unsupervised)
● Expectations for DL tend to match hype not
reality
● Some rare cases exist outside this trend (mainly
in asia)
LSTM
AutoEncoder

12.
Simbox fraud for
telco
● Costs telco over 3 billion yearly
● Route calls for free over a carrier network
● Need to mine raw call detail records to find
● Find and cluster fraudulent CDRs with
autoencoders (unsupervised)
● Beats current rules and supervised based
approaches

13.
Network
Intrusion
● Raw web log traffic
● Detect attacks at points of origin
● Typically supervised learning
● Goal: Classify raw time series to find attacks
● Optional: Detect *kind* of attack

14.
Fintech
securities churn
prediction
● Predict when user is going to leave
service
● Using recurrent nets find likelihood of leaving
● Using lift curves identify budget for sending
discounts to percentage of users “worth” saving
● Optional: use autoencoders with kmeans to
identify groups of users wanting to leave

15.
Corporate
campus security
● At 30 FPS or more find dangerous objects in a
crowd
● Identify a target object and send immediate
report
● Uses variants of Convolutional nets
● Imagine hooking this up to a real camera

16.
Conclusion
● Deep Learning still young
● Many use cases not being tried
● Research is moving faster every year
● Talent still hard to find
● Will become more common with time