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Arshak Navruzyan
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Jerry Guo is attending Arshak Navruzyan's event

The Hardware of Deep Learning at General Assembly

August 19, 2017 all day
From Nvidia's latest GPUs to Intel's Lake Crest and Google's TPUs, there is a plethora of options for training deepnets.  Should you build your own GPU rig or is it better to use the cloud?  Where should you train models?  What about inference at scale? This conference will focus on some best practices for deploying deep learning models into production on a variety of hardware and cloud platforms.  Speakers will discuss topics like:Recent benchmarks of popular modelsExisting and new GPU…See More
Jun 27
Arshak Navruzyan posted an event

The Hardware of Deep Learning at General Assembly

August 19, 2017 all day
From Nvidia's latest GPUs to Intel's Lake Crest and Google's TPUs, there is a plethora of options for training deepnets.  Should you build your own GPU rig or is it better to use the cloud?  Where should you train models?  What about inference at scale? This conference will focus on some best practices for deploying deep learning models into production on a variety of hardware and cloud platforms.  Speakers will discuss topics like:Recent benchmarks of popular modelsExisting and new GPU…See More
Jun 18
Arshak Navruzyan's blog post was featured

Honeypot Turing Test

The honeypot is a method of cybersecurity in which a bait (‘honey’) system/network is designed to emulate or act as a real system/network to divert malicious attacks upon the actual real system/network.  The honeypot may act to mitigate, block, and in some cases capture the malicious behavior.  The concept of the honeypot probably originated from two books, “The Cuckoos Egg” by Clifford Stoll and “An Evening with Berferd” by Bill Chewick, both describing the authors’ own personal efforts to…See More
Oct 14, 2016
Arshak Navruzyan posted a blog post

Honeypot Turing Test

The honeypot is a method of cybersecurity in which a bait (‘honey’) system/network is designed to emulate or act as a real system/network to divert malicious attacks upon the actual real system/network.  The honeypot may act to mitigate, block, and in some cases capture the malicious behavior.  The concept of the honeypot probably originated from two books, “The Cuckoos Egg” by Clifford Stoll and “An Evening with Berferd” by Bill Chewick, both describing the authors’ own personal efforts to…See More
Oct 14, 2016
Georges Bressange liked Arshak Navruzyan's blog post Applying ML to InfoSec: Adversarial ML
Jul 14, 2016
Arshak Navruzyan posted a blog post

Applying ML to InfoSec: Adversarial ML

There seems to be very little overlap currently between the worlds of infosec and machine learning. If a data scientist attended Black Hat and a network security expert went to NIPS, they would be equally at a loss. This is unfortunate because infosec can definitely benefit from a probabilistic approach but a significant amount of domain expertise is required in order to apply ML methods.Machine learning practitioners face a few challenges for doing work in this domain including understanding…See More
Jul 13, 2016
Naky Lopez Ribeiro liked Arshak Navruzyan's blog post Detecting Money Laundering with Unsupervised ML
Jul 10, 2016
Arshak Navruzyan posted a blog post

Detecting Money Laundering with Unsupervised ML

Financial institutions have a regulatory requirement to monitor account activity for anti-money laundering (AML). Regulators take the monitoring and reporting requirements very seriously as evidenced by a recent set of FinCEN fines.  One challenge with AML is that it rarely manifests as the activity of a single person, business, account, or a transaction. Therefore detection requires behavioral pattern analysis of transactions…See More
Jun 29, 2016
Arshak Navruzyan posted a blog post

Formulation of Adversarial ML

Machine learning is being used in a variety of domains to restrict or prevent undesirable behaviors by hackers, fraudsters and even ordinary users.  Algorithms deployed for fraud prevention, network security, anti-money laundering belong to the broad area of adversarial machine learning where instead of ML trying to learn the patterns of benevolent nature, it is confronted with a malicious adversary that is looking for opportunities to exploit loopholes and weaknesses for personal gain.Some…See More
Jun 1, 2016
Arshak Navruzyan posted an event
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Adversarial Machine Learning: Fraud, Security, AML at Geekdom SF

September 10, 2016 all day
Machine learning techniques were originally designed for environments in which the training and test data are assumed to be generated from the same (although possibly unknown) distribution and/or process. In the presence of intelligent and adaptive adversaries, however, this working hypothesis is likely to be violated.Applying machine learning to use cases like fraud, security,  anti-money laundering and know your customer (KYC) presents a unique set of challenges:- Little or no labeled data-…See More
May 29, 2016
Muraleedharan P G is attending Arshak Navruzyan's event

Deep Learning Hands-on Workshops at Yelp San Francisco

November 7, 2015 all day
We are devoting an entire conference to deep learning tools and applications. The format will consist of hands-on workshops and brief presentations and discussions about the impact of deep learning.The technical workshops will be led by contributors of leading open source tools like Neon, Keras, Opendeep, etc.  Each attendee will get a EC2 GPU instance with the necessary software pre-loaded for running the exercises.We will also have some panel discussions by leading practitioners that are…See More
Aug 20, 2015
Arshak Navruzyan posted an event

Deep Learning Hands-on Workshops at Yelp San Francisco

November 7, 2015 all day
We are devoting an entire conference to deep learning tools and applications. The format will consist of hands-on workshops and brief presentations and discussions about the impact of deep learning.The technical workshops will be led by contributors of leading open source tools like Neon, Keras, Opendeep, etc.  Each attendee will get a EC2 GPU instance with the necessary software pre-loaded for running the exercises.We will also have some panel discussions by leading practitioners that are…See More
Aug 17, 2015
Arshak Navruzyan posted a blog post

Data Science Fellowship Focused on Practical Experience

You've made up your mind to become a data scientist. You've taken every data science MooC, you've eaten a lifetime of pizza at machine learning meetups, you even attended a data science "academy." Why hasn't it worked?Data Science is not knowledge to be acquired but rather a skill that can be learned and improved through practice. The number one qualification employers look for when hiring a data science candidate is previous experience. Startup.ML is launching a fellowship to give aspiring…See More
Mar 29, 2015
Arshak Navruzyan posted an event

Next.ML Boston Machine Learning Workshops & Case Studies at Microsoft NERD Boston

April 27, 2015 all day
Next.ML Boston Machine Learning Workshops & Case StudiesMicrosoft NERD / Mon April 27, 201511 million people are involved in the software industry today according to . They write complicated, brittle code using a declarative approach that often fails to recognize the complexity of user behavior.Next.ML is an all day event that brings together the leading machine learning engineers from industry and academia to kick off the transition from the…See More
Feb 27, 2015
Arshak Navruzyan posted an event

Next.ML Boston Machine Learning Workshops & Case Studies at Microsoft NERD Boston

April 27, 2015 all day
Next.ML is a conference focused on real-world, practical applications of machine learning.  Discover how leading machine learning practitioners are solving problems.  An estimated 11 million people are involved in the software industry today (according to IDC). They write complicated, brittle software using a declarative approach that often fails to recognize the complexity of the real world and the intricacy of user behavior.The future of software engineering is not declarative, it’s…See More
Jan 24, 2015
NEHA BUNDELA is attending Arshak Navruzyan's event

Machine Learning Workshops on Deep Learning, Probabilistic Programming, Julia and more at San Francisco

January 17, 2015 all day
Next.ML is an annual conference on the latest machine learning research and open source software.  The program consists of hands-on workshops led by practitioners, open source contributors and researchers. Unlike other machine learning conferences that serve mainly as a showcase for the work being done at research labs, the goal of Next.ML is to enable you to use the latest machine learning techniques the minute you leave the conference.    Workshop instructors…See More
Nov 18, 2014

Profile Information

Field of Expertise
Big Data
Professional Status
VP
Interests:
Networking

Arshak Navruzyan's Blog

Distillation of Deepnets

Posted on December 23, 2016 at 7:30am 0 Comments

Training modern deepnets can take an inordinate amount of time even with the best GPU hardware available. Inception-3 on ImageNet 1000 using 8 NVIDIA Tesla K40s takes about 2 weeks (Google Research Blog).

Even when a large network is trained successfully, the memory footprint and the prediction latency (due to the number of its parameters) can make it challenging to put it into production.

One way to keep…

Continue

Probabilistic Pentesting

Posted on November 12, 2016 at 1:00pm 0 Comments

Pentesting tools like Metasploit, Burp, ExploitPack, BeEF, etc. are used by security practitioners to identify possible vulnerability points and to assess compliance with security policies. Pentesting tools come with a library of known exploits that have to be configured or customized for your particular environment.  This configuration typically takes the form of a DSL or a set of fairly complex UIs to configure individual…

Continue

Honeypot Turing Test

Posted on October 12, 2016 at 7:00pm 0 Comments

The honeypot is a method of cybersecurity in which a bait (‘honey’) system/network is designed to emulate or act as a real system/network to divert malicious attacks upon the actual real system/network.  The honeypot may act to mitigate, block, and in some cases capture the malicious behavior.  The concept of the honeypot probably originated from two books, “The Cuckoos Egg” by Clifford Stoll and “An Evening with Berferd” by Bill Chewick, both describing the authors’ own personal…

Continue

Applying ML to InfoSec: Adversarial ML

Posted on July 4, 2016 at 10:00am 0 Comments

There seems to be very little overlap currently between the worlds of infosec and machine learning. If a data scientist attended Black Hat and a network security expert went to NIPS, they would be equally at a loss. 

This is unfortunate because infosec can definitely benefit from a probabilistic approach but a significant amount of domain expertise is required in order to apply ML methods.

Machine learning practitioners face a few challenges for doing work in this domain…

Continue

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