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 applications10:00 - 11:00 Introduction to deep learning and how it is currently being used by enterprises11:00 - 12:00 Question and answer session with each student on deep learning's present or future role in their business12:00-1:00 LunchAfternoon: Overview of deep learning strategies

1:00-2:00 Awareness building - educating specific groups within your enterprise on the maturity of the technology2:00-3:00 Specialized to particular tasks - bespoke, custom, made-to-order solutions3:00-4:00 Using deep learning across teams - capturing and re-using insights, running a deep learning-first enterprise4:00-5:00 Question and answer session with each student on what their business would need out of a deep learning strategy

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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

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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


@edeeplearning

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

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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

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Face Detection

Preview - Slide Available at deeplearningconf.com


Facial Recognition


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http://deeplearningconf.com



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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

Preview - Slide Available at deeplearningconf.com Market segmentation

Preview - Slide Available at deeplearningconf.com July 26 2017





@edeeplearning

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

Preview - Slide Available at deeplearningconf.comMore 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


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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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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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@edeeplearning


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

https://twitter.com/AndrewYNg/status/788548053745569792

https://twitter.com/AndrewYNg/status/788548053745569792


@edeeplearning

What is Machine Learning?

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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 vastquantities 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 coulduse only common

sense, whichyou may havea lot of, if youhave domainknowledge


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So, you pick a decision threshold July 26 2017


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- You can classifydata by only onefeature, and it is

scientific tolook at one

variable, but it’s very limiting!


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- You can classify data by only two

features, and it isinterpretable, but it’s

still limiting!


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- 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.

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- 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.


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@edeeplearning

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

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Part 2 of 7


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Introduction to Deep Learning and How It Is Being Used By Enterprises

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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

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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

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@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

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https://skymind.ai/case-studies/image

Image classification for online experiences, e-commerce, and security


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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

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Image classification for security and enforcement


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@edeeplearning


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 algosJuly 26 2017


@edeeplearning


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


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Intro to Neural Networks (Part 2: Forward Propogation)

Keywords: Hyperparameters, Activation Function, Forward Propagation, Hidden Layers

https://www.youtube.com/watch?v=UJwK6jAStmg


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@edeeplearning


Intro to Neural Networks (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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Intro to Neural Networks (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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Intro to Neural Networks (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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Intro to Neural Networks (Part 6: Training)

Keywords: Convergence, Local Minima, Optimization, BFGS, Overfitting,



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Intro to Neural Networks (Part 7: Overfitting, Testing, and Regularization)

Keywords: Signal vs. Noise, Training Data, Testing Data, Regularization



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Part 3 of 7


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Question and Answer Session on Deep Learning’s Current or Future Role In Your Business

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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?

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Part 4 of 7


@edeeplearning

Awareness Building - Educating Specific Groups Within Your Enterprise on the Maturity of the Technology

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

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@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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@edeeplearning

- 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

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@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

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Part 5 of 7


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- 5.5 million face videos from 70 countries


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- 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 companiesJuly 26 2017


@edeeplearning


- Use temporal model, expressions unfold over time


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- 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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- Typically train on 200 examples, run experiments every night


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- 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


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- 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


- 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


- 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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- 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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- 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

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- 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


@edeeplearning


- 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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- Three types of cybersecurity categories to consider: Malware, threat detection, and stream detection


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- 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


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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

Preview - Slide Available at deeplearningconf.comJuly 26 2017



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- 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

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- 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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- Common technique is to look at the file hash - the filename description, in large quantities, to classify a threat


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- Bad actors might add gibberish to a file, that can mislead tools


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- 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


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- Problem: adversaries follow conferences, two to three weeks see big change in variance in

random domains


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- A regex can’t identify these pseudorandom domain names, so unsupervised learning is used


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- 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




@edeeplearning


- 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




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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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- Cover 80k publicly traded companies, 15 million private companies, “track” 200 million people, current litigation,

who investors are


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- 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


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@edeeplearning


- 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

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- 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


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- 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


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- 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

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Part 6of 7


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Using Deep Learning Across Teams - Capturing and Re-using Insights, Running a Deep-Learning First Enterprise

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@edeeplearning

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


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@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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@edeeplearning

- 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

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@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

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@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

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@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

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@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

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@edeeplearning

Use a basic metric, and separate from production initially


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@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


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@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


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@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?

1. Choose direct objectives:

Pick a simple objective

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@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?

1. 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

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@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


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@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


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@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


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@edeeplearning

Deep learning across the teams must be calculated


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@edeeplearning

- Diversity, relevance, and personalization are not as correlated with popularity, that is, clicks, as one might think

Look at just your objective


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Look for new data sources once you plateau


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@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


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- 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


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- Engineering time is too valuable to spend time guessing at the importance of certain features as compared to others

Spend engineering time engineering


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- 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

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@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

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@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

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Part 7 of 7


@edeeplearning

Question and Answer Session on What Your Business Would Need Out Of A Deep Learning Strategy

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: [email protected]

Thank you to Google and others who have published guidelines. Slides are for today only.

Executing Deep Learning Strategies

@edeeplearning

July 26 2017

Software

Executing Deep Learning Strategies Masterclass Preview - Enterprise Deep Learning