In this article, an implementation of an efficient graph-based image segmentation technique will be described, this algorithm was proposed by Felzenszwalb et. al. from MIT. The slides on this paper can be found from Stanford Vision Lab.. The algorithm is closely related to Kruskal’s algorithm for constructing a minimum spanning tree of a graph, as stated by the author and hence can be implemented to run in O(m log m) time, where m is the number of edges in the graph.
The number of R packages associated cool new tricks available continues to grow every month. To understand the current state of R packages on CRAN, I ran some code provided by Gergely Daróczi on Github . As of today there have been almost 14,000 R packages published on CRAN and the rate of publishing appears to be growing at an almost exponential trend.Additionally, there are even more packages available on sources like Github, Bioconductor, Bitbucket and more.
In this series, I will talk about training a simple neural network on image data. To give a brief overview, neural networks is a kind of supervised learning. By this I mean, the model needs to train on historical data to understand the relationship between input variables and target variables. Once trained, the model can be used to predict target variable on new input data. In the previous posts, we have written about linear, lasso and ridge regression. All those methods come under supervised learning. But what is special about neural networks is, it works really well for image, audio, video and language datasets. A multilayer neural network and its variations are commonly called deep learning.
Big data and analytics can help a business predict consumer behavior, improve decision-making across the board and determine the ROI of its marketing efforts. By addressing these aspects adequately, the business would not only be able to protect its market share, but also expand into new territories. Below is a detailed look at this topic. To learn more, checkout the infographic below created by Villanova University’s Online Master of Science in Analytics degree program.
Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ Patil as the first chief data scientist to the United Nations using predictive analytics to forecast bombings on schools.
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more.
Here are 29 resources, mostly in the form of tutorials, covering most important topics in data science: This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more.