Executing Deep Learning Strategies Masterclass Preview - Enterprise Deep Learning

Executing Deep
Learning Strategies
Sam Putnam, Enterprise Deep Learning, LLC
July 26, 2017
Day 1

Sam PutnamExecuting Deep Learning Strategies
@edeeplearning
Morning: Introduction to deep learning - High-level overview of why enterprises are
using deep learning
9:00 – 10:00 Introduction to machine learning and its applications

10:00 - 11:00 Introduction to deep learning and how it is currently being used by enterprises

11:00 - 12:00 Question and answer session with each student on deep learning's present or
future role in their business

12:00-1:00 Lunch

Afternoon: Overview of deep learning strategies 
1:00-2:00 Awareness building - educating speciﬁc groups within your enterprise on the
maturity of the technology

2:00-3:00 Specialized to particular tasks - bespoke, custom, made-to-order solutions

3:00-4:00 Using deep learning across teams - capturing and re-using insights, running a deep
learning-ﬁrst enterprise

4:00-5:00 Question and answer session with each student on what their business would need
out of a deep learning strategy

July 26 2017

Part 1 of 7
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July 26 2017
Introduction to Machine Learning and its Applications

Part 2 of 7
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Introduction to Deep Learning and How It Is Being Used
By Enterprises
July 26 2017

Part 3 of 7
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Question and Answer Session on Deep Learning’s Current
or Future Role In Your Business
July 26 2017

Part 4 of 7
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Awareness Building - Educating Specific Groups Within
Your Enterprise on the Maturity of the Technology
July 26 2017

Part 5 of 7
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Specialized to Particular Tasks - Bespoke, Custom, Made-
to-Order Solutions
July 26 2017

Part 6of 7
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Using Deep Learning Across Teams - Capturing and
Re-using Insights, Running a Deep-Learning First Enterprise
July 26 2017

Part 7 of 7
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Question and Answer Session on What Your Business
Would Need Out Of A Deep Learning Strategy
July 26 2017

@edeeplearning
Anomaly Detection
- detecting an insider trade, ﬂagging a keyword
- detecting a fraudulent credit card transaction
- detect money laundering
Machine Learning Applications
Speech Recognition
- detecting speciﬁc words for translation systems
- recognizing your voice, for biometric systems
- identify whale sounds, so ships do not hit them
- detect bird species from a bird call
July 26 2017

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Face Detection
Preview - Slide Available at deeplearningconf.com
Facial Recognition
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Image Classiﬁcation
- tagging images to create a searchable database
- monitoring a stream of images
- augmenting security, helping those who label data
- segment objects, crop photos
Topic Modeling
- automatic tagging of news articles, written content
- detecting a fraudulent transaction,
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Anti-Spam
- detecting (lazy) malware, use system logs/packets
- detect spam in your email, want few false positives
- detecting spam blogs, SEO aggressors
Genetics
- use clustering to ﬁnd disease-predicting genes
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Search
Ads
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Translation
- translate another language to native language
- translate obscure text/writing into readable English
Language understanding
- understand intents/slots from a query
- understand text, forward to appropriate recipient
- branch to different department, issue tracking
- summarize long documents
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Video compression
Weather forecasting
Market segmentation
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Price premiums
- forecast the lifetime cost of an insurance customer
Trade stocks and derivatives
- buy, clean, and backtest data with algorithms
- extract true patterns and detect buy signals
Predict risk of investment
- use key performance indicators
- test and mine valuation models July 26 2017

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Forecast housing/auction sale prices
Predict ratings
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Recognize characters
Self-driving cars
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Factory equipment maintenance
Wait times
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Other forms of fraud
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In-person market segmentation
Chemical drug discovery
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Music recommendations
News or product recommendations
More recommendations
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Employee permissions
- granting or revoking access to speciﬁc applications
- employee attrition
Medical event premonition
- analyze doctors’ notes to predict heart failure
- predict emergency room admissions
- predict premature births
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Call for help
Video Event Detection/Anomaly Event detection
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Design suggestions
- recommend layouts that match color palettes
- colorize black and white images using context
- generate variations of drawing created by user
- create a full photo from a sketch
Identify songs
- hear a few seconds of a song and give the title
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Generate handwriting
- create personalized experiences
Generating Text
- automatically caption images
- generate new ads from previous ad clicks/social
- Fill in missing parts of a legal document
- Generate coherent arguments from legal documents
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Synthesize sound
Generating & Organizing Music
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Question answering
- interact with a chatbot to inquire about an item
- ask your phone questions, receive useful answers
- feature suggestions, gradually expose user via chat
Understand emotion
- See images from a video stream and read emotions
- Identify email or chat messages that are sales leads
- Triage users who need special care or attention
- Personality detection and compatibility July 26 2017

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Individual detection
- enable actions for speciﬁc people in a seat in a car
- personalize/identify user of a family internet account
Predict rising/trending stars
- Mine tags in a geographic area for keywords
- Predict if a product launch will be successful
- Identify credibility of a source or thought leader
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Flag noteworthy news
Authentication
July 26 2017

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Security
- predictive policing using crime data
- imaging and video systems for airport security
- predict psychopathy from internet usage
Energy
- estimating demand requirements, load balancing
- initiating iOT devices to turn on at low peak times
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@edeeplearning
Predict bad loans
- predict reﬁnancers and defaulters
- analyze credit risk and automate loan processing
- conversely reward those most likely to pay back
Combinations of Artistic styles
- combine a famous painting with a camera photo
- modify a live video stream with artistic style
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Detect new market segmentations
Text to speech and speech to text
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Pretty much anything that a normal
person can do in <1 sec, we can now
automate with AI.
– Andrew Ng (@AndrewYNg), 19 October
2016
(with enough developers, even physical motion)
July 26 2017

@edeeplearning
What is Machine Learning?
July 26 2017
A: It Is Teaching a Machine
B: It’s The Engine Behind Artiﬁcial Intelligence (AI)
You Might Believe:
C: It’s What Makes Google Still Relevant and What Makes New
Startups Interesting Enough To Compete (This is a cop out Answer)

@edeeplearning
A: It Is Teaching a Machine
B: It’s The Engine Behind Artiﬁcial Intelligence (AI)
You Might Believe:
C: It’s What Makes Google Still Relevant and What Makes New
Startups Interesting Enough To Compete (This is a cop out Answer)
D: It’s writing code that allows a computer to mine vast
quantities of data and make fast, intelligent decisions
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A: It Is basic intelligence that an analyst can do, but takes
time
B: It is ﬁnding simple patterns in large amounts of data
that humans can’t physically process
often sub-human or comparable
performance that saves money
Other Adequate answers:
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http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
- You could
use only
common
sense, which
you may have
a lot of, if you
have domain
knowledge
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So, you
pick a decision threshold July 26 2017

@edeeplearning
- You can classify
data by only one
feature, and it is
scientiﬁc to
look at one
variable, but it’s
very limiting!
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July 26 2017

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- You can classify
data by only two
features, and it is
interpretable, but it’s
still limiting!
July 26 2017

@edeeplearning
- Question: Why is it
interpretable?
- Answer: You can draw the
decision boundary (see the larger
green and smaller blue rectangles
here), and therefore visualize the
separation between your two
classes.
July 26 2017

@edeeplearning
- So, you could add more features.
- Here, you have seven features, with the relationship between the
features plotted. Look at the top row, what have you learned?
Perhaps still nothing more than the fact that houses in SF (green)
are at higher elevations than houses in NY (blue). But, In most of
the plots, the boundaries that delineate the classes are not
immediately clear. Machine Learning solves this problem for you.
July 26 2017

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What is Deep Learning?
Automate Machine Learning
Automated feature selection
Look at larger quantities of data
Improve accuracy even more in industries that can beneﬁt
from small to medium improvements
July 26 2017

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Deep Learning and How It Is Being Used By Enterprises
Compliance - Bank Secrecy Act Anti-Money Laundering
BSA AML
- cluster suspicious transactions
- improvement on rule-based alert(s), which do not evolve
https://skymind.ai/case-studies/bsa-aml
- can have basic machine learning system that adds rules
July 26 2017

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SIM Box Fraud
https://skymind.ai/case-studies/orange
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https://skymind.ai/case-studies/canonical
SysAdmin Monitoring
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SysAdmin Monitoring Process
https://skymind.ai/case-studies/canonical
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Fraud Monitoring
https://skymind.ai/case-studies/ﬁnance
- rule based systems used because tens to hundreds of
authorizations per second are sent to be vetted
- instead of sampling fraud instances and losing information,
collect and learn from all of the information
July 26 2017

@edeeplearning
- roll your own system, rather than relying on
legacy, catch-all analytics systems
- adaptively prioritize high probability cases of frauds
in ways frozen decision tree models do not
Fraud Monitoring
https://skymind.ai/case-studies/ﬁnance
- ﬁnance is already open source - Linux, Hadoop/
Spark
July 26 2017

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https://skymind.ai/case-studies/image
Image classiﬁcation for online experiences, e-commerce,
and security
July 26 2017

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Image classiﬁcation for security and enforcement
- detect logos in images for copyright infringement
- monitor video streams and detect weapons under clothing
- automobile industry is a user of deep learning, detect
scratches on vehicles as they go down assembly line
July 26 2017

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Image classiﬁcation for security and enforcement
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https://skymind.ai/commerce
Recommender Systems
- deal with mix of browsing behavior metadata, support chat
history, and transaction history
- analyze information about pages viewed, text in descriptions,
product images, type of music played in store
- constantly reﬁne clustering algos
July 26 2017

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Intro to Neural Networks
(Part 1: Data and Architecture)
Keywords: Supervised, Regression, Classiﬁcation, Artiﬁcial
Neural Networks, Hidden Layers, Weights
https://www.youtube.com/watch?v=bxe2T-V8XRs
July 26 2017

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(Part 2: Forward Propogation)
Keywords: Hyperparameters, Activation Function, Forward
Propagation, Hidden Layers
https://www.youtube.com/watch?v=UJwK6jAStmg
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(Part 3: Gradient Descent)
Keywords: Cost Function, Training, Dimensionality, Brute
Force, Gradient Descent, Non-Convex, Stochastic Gradient
Descent
https://www.youtube.com/watch?v=5u0jaA3qAGk
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(Part 4: Backpropagation)
Keywords: Chain Rule, Activation, Backprogagating, Batch
Gradient Descent, Deep Neural Network,
https://www.youtube.com/watch?v=GlcnxUlrtek
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(Part 5: Numerical Gradient Checking)
Keywords: The Deﬁnition of the Derivative, Vectors, Perturb
Weights, Norm
https://www.youtube.com/watch?v=pHMzNW8Agq4
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(Part 6: Training)
Keywords: Convergence, Local Minima, Optimization, BFGS,
Overﬁtting,
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(Part 7: Overfitting, Testing, and Regularization)
Keywords: Signal vs. Noise, Training Data, Testing Data,
Regularization
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1) What is your name, again, and what is your business?
2) What do you see as deep learning’s current or future
role in your business?
3) What questions do you have about deep learning in
production that we can we talk through?
July 26 2017

@edeeplearning
- No decision is the default for machine learning systems - that
is Ok
If the current system is A, then the team would be unlikely to switch to B.
If the current system is B, then the team would be unlikely to switch to A.
This seems in conﬂict with rational behavior: however, predictions of changing metrics
may or may not pan out, and thus there is a large risk involved with
either change.
Start with Expectations and Metrics
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
July 26 2017

@edeeplearning
- You may have a number of project requirements and metrics,
but you start with one machine learning objective function
- If you optimize for number of clicks, you are likely to see the
time spent increase. So, keep it simple and don’t think too
hard about balancing different metrics when you can still
easily increase all the metrics.
Have 1 Objective Only
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Launch first, get data and do machine learning after
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Do best to capture all information
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Keep it simple
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- Have speciﬁc people own a feature/column and document
how it is populated and what it is
- Although many feature columns have descriptive names, it's
good to have a more detailed description of what the feature
is, where it came from, and how it is expected to help.
Segment ownership of the project to data engineering and data science
July 26 2017

@edeeplearning
- Prioritize the freshness requirement of your model, if it
generates a majority of your revenue and you've seen a major
performance hit when its unwatched, watch it more often
- Ad systems receive new ads each day, so generally must
update daily
Freshness is paramount
July 26 2017

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to-Order Solutions
- 5.5 million face videos from 70 countries
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to-Order Solutions
- Quantify gaze ﬁxation duration, but also discrete emotions
(happy, sad, perplexed)
- Partner company, HireVue uses tech to rank video interviews
- Recently started to use voice data as well,
another modality, conversational commerce,
healthcare proven by other companies
July 26 2017

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to-Order Solutions
- Use temporal model, expressions unfold over time
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to-Order Solutions
- Using Convolutional Neural Nets, training on identical images
with different lighting for robustness to daylight/nighttime
- All data goes to Amazon S3
- Certiﬁed labelers spit into training and validation, if agree
- Also use active machine learning, so
machine in the loop, humans decide outliers
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to-Order Solutions
- Typically train on 200 examples, run experiments every night
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to-Order Solutions
- Think car on autopilot, in seconds needs to know if you’re
awake or not to take control
- Think application that reads your emotions
as you view a billboard, car iOT
July 26 2017

@edeeplearning
to-Order Solutions
- Matters because techniques are very applicable to audio,
using analog detectors to get audio waveform out, extracting
the signature of a particle, as in speech
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@edeeplearning
to-Order Solutions
- Cofounder recorded himself 24/7 for two weeks, uploaded
the data every day
- Speech to Text didn’t work well enough, average microphone
caused high word error rate
- Google had worked on this problem in the past, turning
political speeches to text, but didn’t work July 26 2017

@edeeplearning
to-Order Solutions
- Google went back to text to speech
- Deepgram folks tried to recognize features directly, started at
20% accuracy, went to 90% six months later
- Think searching/indexing recorded business calls that would
otherwise be useless low quality audioJuly 26 2017

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to-Order Solutions
- Not using text as an intermediary, building the index out of
activations in the deep neural network
- Similar to using phonemes as features, but doesn’t exactly
learn same “phonemes”
- Can force an ASR system to use phonemes to get slightly
better performance in cases July 26 2017

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to-Order Solutions
- Same idea as CNNs, hierarchical representations, deeper is
higher level, as in phonemes versus indiscernible sounds
- Can train with labeled topics and part of speech to further
restrict and control what the activations are
- Can look at 2D FFT and recognize patterns, like images
July 26 2017

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to-Order Solutions
- In the tens of layers could go deeper with much more
computation, but need to think where to put skips and 90% to
92% not that attractive at this time
- Service is an API, send in query and get all mentions
- Can get customized model built, needs 50 to 100 ten minute
to one hour long ﬁles at least July 26 2017

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to-Order Solutions
- Own model runs on top of Theano, Caffe, and TensorFlow,
can add 2D convolutional layers, another with batch norm,
recurrent layers with batch norm, then dense layer that
predicts the word
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to-Order Solutions
- Three types of cybersecurity categories to consider: Malware,
threat detection, and stream detection
July 26 2017

@edeeplearning
to-Order Solutions
- Ground truth is really expensive, unlike the image and video
data applications, where data is plentiful
- To get the “bad” data, pay a provider for a threat database
July 26 2017

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- Clients are antivirus companies and police organizations
- Need an expert to tell you that is a threat, must reverse
engineer it
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to-Order Solutions
- Have a labeled database, so run an ensemble regression on
the inputs, use scoring altos to see how serious the data is
coming from the feed/stream
- Also collect data with “honeypots” - vulnerable machines put
out as bait, get info on attackers
July 26 2017

- Have both host and network based data to look
at, common to look at function names and regex
for “bad” strings
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to-Order Solutions
- Common technique is to look at the ﬁle hash - the ﬁlename
description, in large quantities, to classify a threat
July 26 2017

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to-Order Solutions
- Bad actors might add gibberish to a ﬁle, that can mislead
tools
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to-Order Solutions
- Plus, calls to 1000 domains, picks one or two to attahk,
modiﬁes domains using domain generation algos, which
humans can easily see through, but not hundreds of
thousands a day
July 26 2017

- Problem: adversaries follow conferences, two
to three weeks see big change in variance in
random domains
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to-Order Solutions
- A regex can’t identify these pseudorandom domain
names, so unsupervised learning is used
July 26 2017

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to-Order Solutions
- Develop predictive models that cover all major trading
markets in the world, 30k stocks, forecast over 7 days, 14
days, and 30 days, seeing 60% to 70% accuracy four
months in, so far
July 26 2017

@edeeplearning
to-Order Solutions
- Macroeconomic effects - Something in Asia ripples
through the US ﬁnancial markets
- Bringing technology to hedge fund quants as early
adopters
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- Ultimately make accessible to investment managers - want
capabilities exposed via natural language
July 26 2017

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- Traditional model can’t represent graph-directed edges,
monitoring a million pieces of data a day
- Structured ﬁnancial data and unstructured regulatory
ﬁlings
- 300k news articles a day also - topic modeling
July 26 2017

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to-Order Solutions
- Cover 80k publicly traded companies, 15 million private
companies, “track” 200 million people, current litigation,
who investors are
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to-Order Solutions
- Entity resolution, disambiguating between sea shells and
Shell the company, record linkage links datasets together,
connecting things based off of location, or mention of an
iPhone, even when Apple not mentioned
July 26 2017

@edeeplearning
to-Order Solutions
- Grab every patent of every US company, check if address
matches other company ﬁlings, look at these bodies of
evidence to prove a linkage
- Automated information extraction a real challenge, use
deep learning with TensorFlow for language
understanding, and to extract similar entities
July 26 2017

@edeeplearning
to-Order Solutions
- Get better results using thousands of highly specialized
models than one general AI model
- First network picks out a concept/topic from the text, then
a more sophisticated model determines that the document
is about, say, a corporation.
July 26 2017

@edeeplearning
to-Order Solutions
- Given word embeddings, corporation is near M & A, strong
relationship there, so can identify that original company is
involved in M & A activity.
- Think automated tone identiﬁcation to augment
committees of users who currently evaluate CFO’s body
language and mannerisms July 26 2017

@edeeplearning
to-Order Solutions
- Bottom line - deep learning at the onset for topic modeling
and summarization, interpretable models at the user-facing
end for the forecasting
- Launched product, using an ensemble model so that is
more “reverse engineerable”, retrain when you ﬁnd you
overweighted interest rates, but try to be hands free, 60%
is good enough oner an entire portfolio
July 26 2017

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to-Order Solutions
July 26 2017

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Use Analytics
- Use analytics, choose simple features you don’t know you will
need yet, track your current system with metrics
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
July 26 2017

@edeeplearning
- Choose simple features so that you can be assured they are
reaching your algorithm correctly when you have a mix of live
and ofﬂine-collected features
Create your pipeline
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@edeeplearning
- Have permission to manually inspect data to double check?
Do it.
- Have tests for examples in training and serving and check
that the score is the same for an example
Test, independently
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- If data is coming from different services in your company,
include a feature that speciﬁes this
Include most data
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@edeeplearning
- Use preexisting systems for preprocessing for your new
system, a sender that has been blacklisted should be labeled
such, not learned
- Create a feature that encodes the heuristic, a relevance
score can be encoded as a feature for a search system, just
as a tax assessment can be encoded for a housing prices
model
Use what you have previously learned
July 26 2017

@edeeplearning
- Do create composite heuristics that well represent your
scoring function, for example, multiple the rating by the
number of installs
- Try breaking apart a composite heuristic of unrelated
features, say one that sums installs and number of characters
in an app description, and use each of those parts as features
Cautiously use combined heuristics
July 26 2017

@edeeplearning
- If you have a signiﬁcant difference between training
performance and holdout performance, look to training the
next iteration of your model using data that is from different
days
- Codify positional features, you will sidestep feedback loops
that come from placing, for an example, an ad in the ﬁrst
position and seeing it get clicked and therefore weighted more
heavily
Check for data collection missteps and feedback loops
July 26 2017

@edeeplearning
- Have higher regularization on features that correspond to
more than one query, that way you can emphasis results that
respond only to speciﬁc queries
- Only allow features to have positive weights; therefore, only
“good” features will be used, no feature will have a more
negative impact than an unknown feature
Make informed choices for parameters
July 26 2017

@edeeplearning
- The data bottleneck becomes apparent with unbalanced
classes. If you sampling a tiny percentage, 0.01%, and you
have few spam examples, you must uphold the sampling rate
between classes. You can work with as few as 10k examples,
sometimes fewer
You don’t need all the data, if you have a ton
July 26 2017

@edeeplearning
Use a basic metric, and separate from production initially
July 26 2017

@edeeplearning
- Gradually decaying performance can indicate a table is not
being refreshed, so a feature has signiﬁcantly and/or older
examples, so a refresh can improve performance vastly more
than improving the model
Even the giants fail, conspicuously in this case
July 26 2017

@edeeplearning
Pick one proxy objective and stick with it at the onset
- If your objective is increased, but you have chosen not to
launch the product, reevaluate or pick a new objective that
when increased will result in a launch
July 26 2017

@edeeplearning
1. Was this ranked link clicked?
2. Was this ranked object downloaded?
3. Was this ranked object forwarded/replied to/emailed?
4. Was this ranked object rated?
5. Was this shown object marked as spam/pornography/
offensive?
Choose direct objectives:
Pick a simple objective
July 26 2017

@edeeplearning
1. Did the user visit the next day?
2. How long did the user visit the site?
3. What were the daily active users?
. Avoid modeling indirect effects, these are metrics, to be used
during A/B testing during launch, not objectives:
Watch out for roundabout or indirect objectives
July 26 2017

@edeeplearning
- Get as many metrics as possible, combine and recombine
features to create new features, just like that embraced by
Kaggle
Iterate on features for your models
July 26 2017

@edeeplearning
- Use deep features after you have a baseline system using
manually selected features, as a deep model can give a
different solution the next time it is trained, so it needs to be
tested more thoroughly
Use deep features after you have developed a baseline
July 26 2017

@edeeplearning
- It is expected that some of the labels will be wrong or cover
different amounts of data, use a lot of these simple features
anyway. You can use “regularization”, that is, add noise, to
learn in a generalizable way, despite this.
Use all the data you have, but nothing you wouldn’t have
July 26 2017

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Deep learning across the teams must be calculated
July 26 2017

@edeeplearning
- Diversity, relevance, and personalization are not as
correlated with popularity, that is, clicks, as one might think
Look at just your objective
July 26 2017

@edeeplearning
Look for new data sources once you plateau
July 26 2017

@edeeplearning
- If you can use one programming language across training
and serving, do it, it will allow you to share code and better
conﬁrm performance
Simplify and do not cross models
July 26 2017

@edeeplearning
- Pick a number of features that represent the length, such as
number of unique words, number of words, number of
characters, number of pages, for example, let the system
decide what is important.
Pick multiple features, let the system learn
July 26 2017

@edeeplearning
- Engineering time is too valuable to spend time guessing at
the importance of certain features as compared to others
Spend engineering time engineering
July 26 2017

@edeeplearning
- Get rid of features that are not clean or interpretable as to
how they were collected, or that cover a tiny percentage of the
examples
- At the same time, deﬁnitely keep those features that cover a
tiny percentage of the examples but a large percentage of a
certain class
Clean up your features if you can
July 26 2017

@edeeplearning
- The number of feature weights you can learn, that is, the
number of parameters, is roughly proportional to the amount of
data you have
- Yes, more data means more features, which tends to mean
better performance. As a general rule, go two orders of
magnitude down from number of examples to number of
features
Follow general rules
July 26 2017

@edeeplearning
- Preprocess data using “discretizations" and “crosses”.
- Discretizations create boundaries of histograms, pick a
discrete integer to represent a grouping
- With a large amount of data, use crosses to create new
features that are unions/overlaps between 2 or more features
Use transformations to augment data
July 26 2017

@edeeplearning
1) What do you see as your company’s deep learning
strategy?
2) What questions do you have about deep learning
strategies that we can we talk through?
3) What is something that you are going to take away
from this conference and apply to your business?
July 26 2017

Thank you
Sam Putnam
Questions/Comments: Sam@EDeepLearning.com
Thank you to Google and others who have published guidelines. Slides
are for today only.
Executing Deep Learning Strategies
@edeeplearning
July 26 2017

Executing Deep Learning Strategies Masterclass Preview - Enterprise Deep Learning

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