Big Data Developers in Madrid - Deep Learning for Everyone & Building high performing weighted XGBoost ML models
Tuesday 13 November 2018, 18:30 - 19:30
Calle Corazón de María, 44 (esquina con Santa Hortensia), Madrid, España
Traemos a nuestros Ingenieros de IBM Center for Open-source Data & AI Technology de Silicon Valley!!(sesiones en Inglés) No te lo puedes perder si te interesa todo lo relacionado con AI/ Machine Learning en el marco de las tecnologías Open Source! ** IMPORTANTE, no te olvides de traer tu DNI u otro documento identificativo para poder acceder por el control de seguridad ** 18:30 – 18:50. "Intro y bienvenida" – Victoria Gómez, IBM Big Data sales leader para Europa ** 18:50 - 19:40. "Deep Learning for Everyone" - Nick Pentreath, Principal Engineer at IBM’s Center for Open-source Data & AI Technology. Committer and PMC member of the Apache Spark project and author of the book "Machine Learning with Spark" ** 19:40 - 20:30. "How to build high performing weighted XGBoost ML models" - Alok Singh, Principal Engineer at IBM’s Center for Open-source Data & AI Technology ** 20:30 - 21:00 - Pizzas y networking TE ESPERAMOS!!! **Title: Deep Learning for Everyone: The IBM Developer Model Asset ExchangeAbstract:We’ve all heard that AI is going to become as ubiquitous in the enterprise as the telephone, but what does that mean exactly? Everyone in a company has a telephone; and everyone knows how to use their telephone; and yet the company isn’t a phone company. How do we bring AI to the same standard of ubiquity —where everyone in a company has access to AI and knows how to use AI; and yet the company is not an AI company?In this talk, we’ll break down the challenges a domain expert faces today in applying AI to real-world problems. We’ll talk about the challenges that a domain expert needs to overcome in order to go from “I know a model of this type exists”to “I can tell an application developer how to apply this model to my domain.” We’ll conclude the talk with a live demo that show cases how a domain expert can cut through the stages of model deployment in minutes instead of days using the IBM Developer Model Asset Exchange (donated to Open Source) Speaker:Nick Pentreath is a principal engineer in IBM’s Center for Open-source Data & AI Technology (CODAIT), where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations. He has also worked at Goldman Sachs, Cognitive Match, and Mxit. He is a committer and PMC member of the Apache Spark project and author of Machine Learning with Spark. Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value ** Title: How to build high performing weighted XGBoost ML model for real life imbalance datasetAbstract:In this talk we will illustrate how the Machine Learning classification is performed using XGBoost, which is usually a better choice compared to logistic regression and other techniques. We will use a real life data set which is highly imbalanced (i.e the number of positive sample is much less than the number of negative samples).Class imbalance is a common problem in data science, where the number of positive samples are significantly less than the number of negative samples. As data scientists, one would like to solve this problem and create a classifier with good performance. XGBoost (Extreme Gradient Boosting Decision Tree) is very common tool for creating the Machine Learning Models for classification and regression. However, there are various tricks and techniques for creating good classification models using XGBoost for imbalanced data-sets that is non-trivial and the reason for developing this tutorial Speaker:Alok Singh is a Principal Engineer at the IBM CODAIT Center for Open-Source Data & AI Technologies. He has built and architected multiple analytical frameworks and implemented machine learning algorithms along with various data science use cases. His interest is in creating BigData and scalable machine learning software and algorithms. He has also created many DataScience based applications
Publicado por: Betabeers