background preloader
Related:  Machine LearningData analysis

Machine Learning in Gradient Descent In Machine Learning, gradient descent is a very popular learning mechanism that is based on a greedy, hill-climbing approach. Gradient Descent The basic idea of Gradient Descent is to use a feedback loop to adjust the model based on the error it observes (between its predicted output and the actual output). The adjustment (notice that there are multiple model parameters and therefore should be considered as a vector) is pointing to a direction where the error is decreasing in the steepest sense (hence the term "gradient"). Notice that we intentionally leave the following items vaguely defined so this approach can be applicable in a wide range of machine learning scenarios. The ModelThe loss functionThe learning rate Gradient Descent is very popular method because of the following reasons ... Batch vs Online Learning In batch learning, all training will be fed to the model, who estimates the output for all data points. ɳ = ɳ_initial / (t ^ 0.5). Parallel Learning

Junk Charts Tiny Speck Paul Irish Reading and Text Mining a PDF-File in R 0inShare Here is an R-script that reads a PDF-file to R and does some text mining with it: FlowingData | Data Visualization, Infographics, and Statistics Steve Blank Cloud Developer Tips: Practical tips for developers of cloud computing applications. — Shlomo Swidler An Introduction to WEKA - Machine Learning in Java WEKA (Waikato Environment for Knowledge Analysis) is an open source library for machine learning, bundling lots of techniques from Support Vector Machines to C4.5 Decision Trees in a single Java package. My examples in this article will be based on binary classification, but what I say is also valid for regression and in many cases for unsupervised learning. Why and when would you use a library? I'm not a fan of integrating libraries and frameworks just because they exist; but machine learning is something where you have to rely on a library if you're using codified algorithms as they're implemented more efficiently than what you and I can possibly code in an afternoon. Efficiency means a lot in machine learning as supervised learning is one of the few programs that is really CPU-bound and can't be optimized further with I/O improvements. 1.J48 classifier = new J48(); 2.classifier.setOptions(new String[] { "-U" }); With respect to: 1.SVM classifier = new SMO(); 3. 01.double targetIndex;

Normal Deviate