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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 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 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 Official VideoLectures.NET Blog » 100 most popular Machine Learning talks at VideoLectures.Net Enjoy this weeks list! 26971 views, 1:00:45, Gaussian Process Basics, David MacKay, 8 comments7799 views, 3:08:32, Introduction to Machine Learning, Iain Murray16092 views, 1:28:05, Introduction to Support Vector Machines, Colin Campbell, 22 comments5755 views, 2:53:54, Probability and Mathematical Needs, Sandrine Anthoine, 2 comments7960 views, 3:06:47, A tutorial on Deep Learning, Geoffrey E. Hinto3858 views, 2:45:25, Introduction to Machine Learning, John Quinn, 1 comment13758 views, 5:40:10, Statistical Learning Theory, John Shawe-Taylor, 3 comments12226 views, 1:01:20, Semisupervised Learning Approaches, Tom Mitchell, 8 comments1596 views, 1:04:23, Why Bayesian nonparametrics?, Zoubin Ghahramani, 1 comment11390 views, 3:52:22, Markov Chain Monte Carlo Methods, Christian P. Robert, 5 comments3153 views, 2:15:00, Data mining and Machine learning algorithms, José L.

Statistical Modeling, Causal Inference, and Social Science « Statistical Modeling, Causal Inference, and Social Science - index.html Statistics and the Science Club One of my favorite movies is Woody Allen’s Annie Hall. If you’re my age and you haven’t seen it, I usually tell people it’s like When Harry Met Sally, except really good. The movie opens with Woody Allen’s character Alvy Singer explaining that he would “never want to belong to any club that would have someone like me for a member”, a quotation he attributes to Groucho Marx (or Freud). Last week I posted a link to ASA President Robert Rodriguez’s column in Amstat News about big data. When discussing what statisticians need to learn, he focuses on technological changes (distributed computing, Hadoop, etc.) and the use of unstructured text data. I agree with this, but I don’t think it goes nearly far enough. The key element missing from the column was the notion that statistics should take a leadership role in this area. There’s a strong tradition in statistics of being the “outsiders” to whatever field we’re applying our methods to.

Angular.js: Autocomplete and enabling a form with $watch and blur | Grobmeier on Dart, Java, Struts, PHP and more I have that small form, consisting of an jQuery autocomplete and a submit button. When the user selects something from the autocompleter, I want to store an ID in the model. Only when this ID is set the submit button should be enabled. It sounds easy in first glance, and it is, if you know how. But first you face these problems: Autocomplete is from jQuery UI. For the model validation you can choose between three different approaches. You could use ng-change. The form and the autocompleter First, lets look at the form. For now, I just added a new attribute named "autocomplete". The plan is to create another model named "myModelId" to store the ID which comes from the autocomplete. Here comes my basic controller. function MyController ($scope) { $scope.myModel = null; $scope.myModelId = null; $scope.isDisabled = true;} Now it is interesting to look at what my server would deliver in response to a successful request of the kind It looks like this: It's JSON.