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Machine Learning Video Library - Learning From Data (Abu-Mostafa)

Machine Learning Video Library - Learning From Data (Abu-Mostafa)
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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.

Getting Started with Neural Network Toolbox Accelerating the pace of engineering and science Contents Documentation Center Getting Started with Neural Network Toolbox Tutorials About Neural Networks Was this topic helpful? © 1994-2014 The MathWorks, Inc. Try MATLAB, Simulink, and Other Products Get trial now Join the conversation Large-Scale Machine Learning with Apache Spark jinbow/Octopus Courses and Tutorials - Machine Learning Resources From Machine Learning Resources *Dr. Tom Mitchell, CMU : *Dr. Chris Atkinson, CMU (Old Link) : [1] *Dr. This section provides links to various tutorials on machine learning and its sub-fields, which are available online. [ edit ] Tutorials General Semi-Supervised Learning: Bayesian Methods for Reinforcement Learning: Group Theoretical Methods in Machine Learning: Online Learning for Real World Problems: Machine Learning for Computer Graphics:

NARX All the specific dynamic networks discussed so far have either been focused networks, with the dynamics only at the input layer, or feedforward networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network. The NARX model is based on the linear ARX model, which is commonly used in time-series modeling. The defining equation for the NARX model is where the next value of the dependent output signal y(t) is regressed on previous values of the output signal and previous values of an independent (exogenous) input signal. There are many applications for the NARX network. Before showing the training of the NARX network, an important configuration that is useful in training needs explanation. The following shows the use of the series-parallel architecture for training a NARX network to model a dynamic system. The goal is to develop a NARX model for this magnetic levitation system.

Level-Up Your Machine Learning Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn? Excellent question! I then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, "Oh, I watch what I eat and consistently exercise." But why textbooks? In this brief roadmap, I list a few excellent textbooks for advancing your machine learning knowledge and capabilities. Also, if you want alternative learning resources, Metacademy is at your disposal as are all of these textbooks. My sister, an artist and writer by trade, asked me how she could understand the basics of data science in a nontrivial way. Expectations: You'll understand some common machine learning algorithms at a high-level, and you'll be able to implement some simple algorithms in Excel (and a bit in R if you get through the entire book). Key Chapters: Know and love chapters 1-12.1.

The art of writing science - Iceweasel 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 – ‘Internet search queries’ (Photo credit: Engineering at Cambridge)3018 views, 4:35:51, Graphical Models, Variational Methods, and Message-Passing, Martin J.

Dynamic Networks This topic discusses how the format of input data structures affects the simulation of networks. It starts with static networks, and then continues with dynamic networks. The following section describes how the format of the data structures affects network training. There are two basic types of input vectors: those that occur concurrently (at the same time, or in no particular time sequence), and those that occur sequentially in time. For concurrent vectors, the order is not important, and if there were a number of networks running in parallel, you could present one input vector to each of the networks. For sequential vectors, the order in which the vectors appear is important. Simulation with Concurrent Inputs in a Static Network The simplest situation for simulating a network occurs when the network to be simulated is static (has no feedback or delays). To set up this linear feedforward network, use the following commands: The commands for these assignments are A = net(P) A = 5 4 8 5

Coursera: Machine Learning Metacademy is an open source platform designed to help you efficiently learn about any topic that you're interested in---it currently specializes in machine learning and artificial intelligence topics. The idea is that you click on a concept that interests you, and Metacademy produces a "learning plan" that will help you learn the concept and all of its prerequisite concepts that you don't already know. Metacademy's learning experience revolves around two central components: You can tell Metacademy that you understand a [prerequisite] concept by clicking the checkmark next to the concept's title in the graph or list view. Coursera Roadmap This roadmap is a supplement to Andrew Ng's Coursera machine learning course. PS) After completing this course, I highly recommend furthering your machine learning knowledge via Roger Grosse's excellent Bayesian Machine Learning Roadmap. Section I (Introduction) Section II (Linear Regression with One Variable ) Section III (Linear Algebra Review) What next?

OceanColor Home Page 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;

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