BigDataFr recommends: Reinforcement Learning and AI […] If you poled a group of data scientist just a few years back about how many machine learning problem types there are you would almost certainly have gotten a binary response: problem types were clearly divided into supervised and unsupervised. Supervised: You’ve got labeled data (clearly defined examples). […]
Month: août 2017
[arXiv] BigDataFr recommends: Massively-Parallel Feature Selection for Big Data
BigDataFr recommends: Massively-Parallel Feature Selection for Big Data […] We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as […]
[arXiv] BigDataFr recommends: Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments
BigDataFr recommends: Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments […] Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kinds of environments with their complexity in terms of heterogeneity […]
[Datasciencecentral] BigDataFr recommends: More on Fully Automated Machine Learning
BigDataFr recommends: More on Fully Automated Machine Learning […] Recently we’ve written a series of articles on Automated Machine Learning (AML) which are platforms or packages designed to take over the most repetitive elements of preparing predictive models. Typically these cover cleaning, preprocessing, some feature engineering, feature selection, and then model creation using one or […]
[arXiv – Ariane Carrance] BigDataFr recommends: Uniform random colored complexes
BigDataFr recommends: Uniform random colored complexes […] We present here random distributions on (D+1)-edge-colored, bipartite graphs with a fixed number of vertices 2p. These graphs are dual to D-dimensional orientable colored complexes. We investigate the behavior of quantities related to those random graphs, such as their number of connected components or the number of vertices […]
[Analyticsvidhya] BigDataFr recommends: 10 Advanced Deep Learning Architectures Data Scientists Should Know!
BigDataFr recommends: 10 Advanced Deep Learning Architectures Data Scientists Should Know! Introduction […] It is becoming very hard to stay up to date with recent advancements happening in deep learning. Hardly a day goes by without a new innovation or a new application of deep learning coming by. However, most of these advancements are hidden […]
[Datasciencecentral] BigDataFr recommends: Do you want to hire a Data Scientist?
BigDataFr recommends: Do you want to hire a Data Scientist? […] As mentioned by Tom Davenport few years back,Data Scientist is still a hottest job of century. Data scientists are those elite people who solve business problems by analyzing tons of data and communicate the results in a very compelling way to senior leadership and […]
[Analyticbridge] BigDataFr recommends: Introducing User Behavioral Analysis in the Risk Process
BigDataFr recommends: Introducing User Behavioral Analysis in the Risk Process […] Many years ago when I was entering the intelligence community, I attended a class in Virginia where the instructor opened the session with a test that I will never forget and that I have applied to almost every analytic task in my career. At […]
[arXiv] BigDataFr recommends: Bayesian Nonlinear Support Vector Machines for Big Data
BigDataFr recommends: Bayesian Nonlinear Support Vector Machines for Big Data […] We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional […]