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How to tackle lack of data: an overview on transfer learning

Data Science Blog

Presumably due to this fact, Andrew Ng, in his presentation in NeurIPS 2016, gave a rough and abstract predictions of how transfer learning in machine learning would make commercial success like white lines in the figure below. The post How to tackle lack of data: an overview on transfer learning appeared first on Data Science Blog.

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Announcing the Winners of ‘The NFL Fantasy Football’ Data Challenge

Ocean Protocol

Fantasy Football is a popular pastime for a large amount of the world, we gathered data around the past 6 seasons of player performance data to see what our community of data scientists could create. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

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The effectiveness of clustering in IIoT

Mlearning.ai

With the emergence of data science and AI, clustering has allowed us to view data sets that are not easily detectable by the human eye. Thus, this type of task is very important for exploratory data analysis. 1207–1221, May 2016, doi: 10.1109/JSAC.2016.2545384. 2016.2545384.

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Multivariate Time Series Forecasting

Mlearning.ai

The Art of Forecasting in the Retail Industry Part I : Exploratory Data Analysis & Time Series Analysis In this article, I will conduct exploratory data analysis and time series analysis using a dataset consisting of product sales in different categories from a store in the US between 2015 and 2018.