2318 results for "earn" across all locations
How Uber Accomplishes Job Counting At Scale
Have more rows than you can count on two hands? Don’t feel like using approximations? Learn how Uber uses Apache Pinot™ to count!
Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System
One-click chat, the Uber driver app’s smart reply system, leverages machine learning to make in-app messaging between driver-partners and riders more seamless.
Attribute-Based Access Control at Uber
Learn about how the core security engineering team defined and implemented an Attribute-Based Access Control policy model at Uber, where 70 services have already adopted it for different authorization needs.
Upgrading Uber’s MySQL Fleet to version 8.0
Learn all about our journey of successfully upgrading our MySQL fleet at Uber from v5.7 to v8.0, enhancing performance and reliability.
Setting the Pace: A Q&A with Jennifer Anderson, Senior Director of Engineering at Uber
Jennifer Anderson, a veteran of Silicon Valley technology companies, leads Uber’s Product Platform organization, which hosts our core services. In this interview, she describes her organization and the lessons she has learned.
Montclair State University students, faculty visit Uber
Nobody knows the ins and outs of using the Uber Driver app better than you. So when we set out to build a better app experience, we asked for you help
The Top 5 Farmers Markets in Toronto
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
M. Li, E. Yumer, D. Ramanan
Current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints. Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e. budgeted training. We analyze the following problem: “given a dataset, algorithm, and resource budget, what is the best achievable performance?” [PDF]
International Conference on Learning Representations (ICLR), 2020
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
Z. Zhao, R. Anand, M. Wang
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant features, accelerating the model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnosis capability. […] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019