In case of Azure Web Jobs or Azure Functions when You try to find alternative for database.SqlQuery<T>() from Entity Framework for Entity Framework Core (equivalent doesn’t exists ) You can use presented below solution in C#. Solution tested and works very good in practice on production environments. Let’s say that we want use below code […]
Read More[Solved] Microsoft Azure: Alternative for database.SqlQuery() from Entity Framework 6.x for Entity Framework Core
As the leading healthcare provider with 169 hostpials, 116 surgury centers, over 200,000 employees,and 26 mllion patient encounters per year, HCA is always looking for ways to improve patient care and manage costs. #ai #machinelearning #ai #deeplearning #ml
Read MoreHCA: Predicting Patient Outcomes in Real-time with H2O
Mobile bankng is a key application for Capital One serving millions of customers per day. Predicting infrastructure issues before they become bottlenecks and impact customer transactions is a key goal of the technology operations group. Effective prediction requires has to scale across large datasets and take into account a variety of time series factors that […]
Read MoreAI & Capital One: Preventing Downtime with Mobile Transaction Forecasing and Anomaly Detection
Fighting fraud at PayPal is a constant battle. From detecting fraud in real-time transactions to catching it before payments are transferred the sophistication of the the approach has to stay ahead of the fraudsters.
Read MorePayPall & Machine Learning in Fraud Detection
This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. The talk will focus on implementing […]
Read MoreTensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O’19)
Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time […]
Read MoreGoogle: The Anatomy of a Production-Scale Continuously-Training Machine Learning Platform
Ulam studiował flzykę pod kierunkiem największych naukowców swoich czasow. Uczeni, którzy zgromadzili się podczas II wojny swiatowej w Los Alamos, należeli do grona czołowych postaci fizyki wspolczesnej. Hans Bethe, Niels Bohr, Enrico Fermi Richard Feynman, Ernest Lawrence, J. Robert Oppenheimer i wielu innych tworzyli zespół, który pod względem potencjalu intelektualnego nie miał sobie równych w […]
Read Moreprof. Stanislaw Ulam i jego prace nad bomba atomowa oraz wodowora
Deep Learning in practice in colorization old videos/movies/photos (black and white) in Real Time – as sample for colorization is using movie from 1935 – “Smierc Jozefa Pilsudskiego”. On left side You can see original movie, on right side colorized movie by AI. System coloring in Real Time. System is running on my personal AI server […]
Read MoreDeep Learning in practice in colorization old videos/movies/photos (black and white) in Real Time
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