Data Science, #ODSC Madrid - WEBINAR: ODSC West Online Warm-Up (Free)
Tuesday 23 October 2018, 22:00 - 23:00
ODSC West is getting closer! We want to invite you to participate in ODSC West's Warm-Up. To access this webinar, please register using the link below:https://attendee.gotowebinar.com/register/3338809288646589441 After our great Part 1, we are bringing 4 new speakers from our ODSC West Conference to present 30 minutes sessions. Matthew Rubashkin, Ph.D. AI Program Director at Insight Data Science: Building an image search service from scratch Michael Mahoney, Ph.D. - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap George Williams, Director of Data Science at GSI Technology, Inc: Visual Search: The Next Frontier of Search Joshua Cook, Curriculum Developer at Databricks: Engineering for Data Science Full Agenda Detail Session 1 - Building an image search service from scratch Speaker:Matthew Rubashkin, PhD Bio: Abstract:We are bringing a workshop on how you would go about building your representations, both for image and text data, and efficiently do similarity search. By the end of this workshop, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset. Session 2 - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap (30 Minutes) Speaker:Michael Mahoney, PhD Bio:Michael Mahoney is at the University of California at Berkeley in the Department of Statistics and the International Computer Science Institute (ICSI). He works on algorithmic and statistical aspects of modern large-scale data analysis. He received his Ph.D. from Yale University with a dissertation in computational statistical mechanics. Abstract:In this session, we will describe some of the underlying randomized linear algebra techniques. We'll describe Alchemist, a system for interfacing between Spark and existing MPI libraries that are designed to address this performance gap. We describe use cases from scientific data analysis that motivated the development of Alchemist and that benefit from this system. We'll also describe related work on communication-avoiding machine learning, optimization-based methods that can call these algorithms, and extending Alchemist to provide an ipython notebook <=> MPI interface. Session 3 - Visual Search: The Next Frontier of Search (30 Minutes) Speaker:George Williams Bio: Abstract:In this session, you will learn the latest state-of-the-art visual search research and techniques as the speakers will share their in-depth knowledge on the subject, how to scale your visual search solution to address the billion-scale problem and how to train models that provide more specific and accurate results for visually rich categories. Session 4 - Engineering for Data Science (30 Minutes) Speaker:Joshua Cook Bio:Joshua Cook is a mathematician. He writes code in Bash, C, and Python and has done pure and applied for computational work in geospatial predictive modeling, quantum mechanics, semantic search, and artificial intelligence. He also has ten years experience teaching mathematics at the secondary and post-secondary level. His research interests lie in high-performance computing, interactive computing, feature extraction, and reinforcement learning. He is always willing to discuss orthogonality or to explain why Fortran is the language of the future over a warm or cold beverage. Abstract:This talk will discuss Docker as a tool for the data scientist, in particular in conjunction with the popular interactive programming platform, Jupyter, and the cloud computing platform, Amazon Web Services (AWS). Using Docker, Jupyter, and AWS, the data scientist can take control of their environment configuration, prototype scalable data architectures, and trivially clone their work toward replicability and communication. This talk will toward developing a set of best practices for Engineering for Data Science.
Publicado por: Betabeers