MLConf 2017 @ ATL

I was happy to visit a great machine learning conference that took place in Atlanta in September. I have eventually found a time and let me share my impression and thoughts  about this event.

The venue for the conference was The Historic Academy of Medicine in Midtown Atlanta. The Academy has a capacity for 230 guests and almost all these places were busy. The place was very accessible. I have never been there but spent only about 40 mins to reach the place using the public transportation system.

High-Performance Deep Learning on Edge Devices With Apache MXNet
Aran Khanna, Software Engineer, Amazon Web Services

 It was impressive. I like the speaker and really interesting topic. There are lots of different mobile devices around us and would be very interesting to use all the power of machine learning on them I think the framework has a big future especially in the area of autonomous devices. Promised flexibility, portability and performance of Apache MXNet look very attractive. The speaker also shows how machine learning algorithms can be implemented in Amazone infrastructure.

Airbnb: Driving a Higher Level of Customer Support with Machine Learning
Yashar Mehdad, Data Scientist Manager, Airbnb

 It was a good speaker and topic was very nice. I like that the topic was related to the real production problem and were provided details how it was solved using machine learning algorithms. The video shows how important feedback could be in machine learning systems.

Making Natural Language Processing Robust to Sociolinguistic Variation
Jacob Eisenstein, Assistant Professor, School of Interactive Computing, Georgia Institute of Technology

 It shows how much useful information we can get do analyzing our social network connections.  Actually, “man is known by the company he keeps”. It works well in the modern world of various social networks and machine learning algorithms.

Game of Drones: Using IoT, Machine Learning, Drones, and Networking to Solve World Hunger
Jennifer Marsman, Principal Software Development Engineer, Microsoft

 This is one of the best presentations on the conference. Data Driven Farming looks like future is already here. Drones on the air and sensors on the ground. Data processed in the cloud and it all works like a single ecosystem. Really really impressed.


Convolutional Neural Networks at scale in Spark MLlib
Jeremy Nixon, Machine Learning Engineer, Spark Technology Center

 The presentation was focused on using machine learning algorithms provided by Spark ML library. If you interesting to know more about this library the video below is right for you.


CompanyDepot: Employer Name Normalization in the Online Recruitment Industry
Qiaoling Liu, Lead Data Scientist, CareerBuilder

 I really like this presentation as it shows real production problem. It always interesting to know how real problem was solved and how it was implemented for production usage.  

Graph Representation Learning with Deep Embedding Approach
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Georgia Tech

Unfortunately, I am not sure if I figured out all technical/math details here.The speaker was very good and provided research results look very impressive.

Beyond a Bit Fit
Tim Chartier, Chief Academic Officer, Tresata

 The presentation shows how machine learning algorithms can be used in such area of our life as a sport. This is a huge area for research. It was exciting to see one of the cases what we have in this area now.

Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification
Jessica Rudd, PhD Student, Analytics and Data Science, Kennesaw State University

Unfortunately, again I am not sure that I got all details about the topic of the presentation. There are lots of researches at the intersection of medicine/biology and machine learning.

Productive Machine Learning and Deep Learning Projects
Greg Werner, Founder & CEO,

It was a very interesting presentation and the speaker is very good. Honestly,  after the conference, I was a little bit shocked realizing how many products for using machine learning algorithms/ deploy machine learning models we already have. This example is one of them. 


Best Practices for Hyperparameter Optimization
Alexandra Johnson, Software Engineer, SigOpt

 In continuation of the previous presentation, this one shows another player on the market machine learning products. The topic is very actual and tips during the presentation look worth to follow.


Codifying Data Science Intuition: Using Decision Theory to Automate Time Series Model Selection
Ryan West, Machine Learning Engineer, Nexosis

 This presentation from another new for me company in machine learning area filed. The presentation traditionally has some useful pieces of advice inside.

Machine Learning Based Attack Vector Modeling for CyberSecurity
Ashrith Barthur, Security Scientist,

That was a really good presentation on topic machine learning and information security. In my understanding anomalies detecting is a quite old but still actual topic. Also, I have to admit H2O platform, as for me it looks very very promising.  


Large-Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention
Venkatesh Ramanathan, Data Scientist, PayPal

This is one of the best presentations on the conference. I really like the content of the presentation. It was shown what PayPal company has now and what are the next steps in future development.

A Machine Learning approach for detecting a Malware
Talha Obaid, Email Security, Symantec

 It was the second presentation on the conference related to machine learning and information security fields. The material was interesting, but honestly speaking I was expecting more about technical details.  


I want to say Thank You to organizers of this conference it was really nice. Thank You to all speakers for their presentations.  And also would like to say Thank You to my company with assistance with tickets for the conference.

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