BigDataFr recommends: Learning to Hash for Indexing Big Data – A Survey ‘The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the […]

BigDataFr recommends: SMART -An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments ‘Abstract The amount of data that human activities generate poses a challenge to current computer systems. Big data processing techniques are evolving to address this challenge, with analysis increasingly being performed using cloud-based systems. […] SMART offers a framework […]

BigDataFr recommends: Three best practices for building successful data pipelines ‘Building a good data pipeline can be technically tricky. As a data scientist who has worked at Foursquare and Google, I can honestly say that one of our biggest headaches was locking down our Extract, Transform, and Load (ETL) process. At The Data Incubator, our […]

BigDataFr recommends: Train faster, generalize better – Stability of stochastic gradient descent ‘We show that any model trained by a stochastic gradient method with few iterations has vanishing generalization error. We prove this by showing the method is algorithmically stable in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex […]