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Data Science

Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization.

From Topological Data Analysis to Deep Learning: No Pain No Gain

Today, I’ll try to give some insights about TDA (for Topological Data Analysis), a mathematical field quickly evolving, that will certainly soon be completely integrated into machine-/deep- learning frameworks. Some use-cases will be presented in the wake of this article, in order to illustrate the power of that theory ! Quick History Topological Data Analysis, also [...]

Google Dataset Search Launched to Help Analysts Scour Repositories

Google Dataset Search is a new product in the beta phase that you can use to find datasets published online. The single interface allows you to search repositories worldwide. Imagine you start a new analytics project. For example, let’s say you want to explore numbers pertaining to Boston Public Schools. Before you would search for it in [...]

A quick look at data visualization for Machine learning by Google...

The 32nd annual NeurIPS (Neural Information Processing Systems) Conference 2018 (formerly known as NIPS), is currently being hosted in Montreal, Canada this week. The Conference is the biggest machine learning conference of the year that started on 2nd December and will be ending on 8th December. It will feature a series of tutorials, invited talks, [...]

Optimization algorithms and methods

Subcategories This category has the following 8 subcategories, out of 8 total. D ► Decomposition methods‎ (7 P) ► Dynamic programming‎ (44 P) E ► Evolutionary algorithms‎ (4 C, 46 P, 2 F) ► Exchange algorithms‎ (10 P) G ► Gradient methods‎ (13 P) L ► Least squares‎ (38 P) ► Linear programming‎ (1 C, [...]

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been...

An empirical evaluation of imbalanced data strategies from a practitioner’s point...

This research tested the following well known strategies to deal with binary imbalanced data on 82 different real life data sets (sampled to imbalance...

Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development

By Kevin Stumpf, Stepan Bedratiuk, and Olcay Cirit As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating powerful new types of ML models to [...]

Clustering and Learning from Imbalanced Data

ArXiv article A learning classifier must outperform a trivial solution, in case of imbalanced data, this condition usually does not hold true. To overcome this...

Introducing TigerGraph Cloud: A database as a service in the Cloud...

Today, TigerGraph, the world’s fastest graph analytics platform for the enterprise, introduced TigerGraph Cloud, the simplest, most robust and cost-effective way to run scalable graph analytics in the cloud. With TigerGraph Cloud, users can easily get their TigerGraph services up and running. They can also tap into TigerGraph’s library of customizable graph algorithms to support key use cases including AI and Machine [...]