In search of better Deep Recommendation systems

Apr 26, 2018 · Addison, United States of America

Hello Makers!

SK Reddy is coming to Dallas! Join us this evening as he discusses deep recommendation systems.

This meetup is in partnership with our friends at Data Science Salon who will also be hosting an all-day informative session the very next day. More Details here: https://datascience.salon/dallas4-18/

Abstract:

With the exponential increase in use and access of online shops, online music, video and image libraries, search engines and recommendation system are the best ways to find what you are looking for (and sometimes, what you are NOT looking for). Meanwhile Deep learning has made significant advances in speech, numeric, text and image processing.

I will share the recent advances made in the field of recommendation systems using deep learning.

Typical recommendation systems can be organized into three groups: Collaborative system, Content based system and Hybrid system.

I will discuss a few recommendation systems coupled with deep learning methods in several application domains.

Attendees: Beginners and Intermediate skilled in Recommender systems

What to expect: The attendees will understand the concepts and discuss the building blocks of Recommender Systems. The attendees will also get to evaluate a few systems and learn techniques to enhance the recommender systems.

Profile of the speaker:

SK is the Chief Product officer AI in Hexagon (www.hexagon.com (http://www.hexagon.com/)). He is an AI and ML expert and a successful twice startup entrepreneur. Also, he is a frequent speaker in conferences and is a ML blogger.

LinkedIn: http://www.linkedin.com/in/sk-reddy/

Blogs: http://www.linkedin.com/in/sk-reddy/detail/recent-activity/posts/

YouTube Video: https://www.youtube.com/user/skreddy99/videos

Slides: https://www.slideshare.net/SKReddy1

GitHub: https://github.com/skreddy99

Event organizers
  • Dallas Big Data Science!

    This meetup is about sharing knowledge & learning the latest developments about statistics, machine learning, math, analytics, parallel algorithms, distributed systems. We will do hands on work with Data and invite experts in the space to discuss practical and theoretical aspects of doing data science with big data.

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