Angular.js: Autocomplete and enabling a form with $watch and blur 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. Getting Started with Neural Network Toolbox Accelerating the pace of engineering and science Contents Documentation Center
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! Index of /files/pour_lucile Index of /files/pour_lucile Name Last modified Size Description Parent Directory - Courses and Tutorials - Machine Learning Resources From Machine Learning Resources *Dr. Tom Mitchell, CMU : *Dr. Chris Atkinson, CMU (Old Link) :  *Dr. 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
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:
Produce Converter: How Much is In It? Are you tired of recipes that call for "The Juice of 1 Lime" when you only have bottled lime juice. Can't figure out how many onions you need to buy so you can get "2 Cups of Onion, Chopped"? This site will solve all your produce conversion questions! One of the biggest hassles when cooking and working in the kitchen is when a recipe calls for "the juice of 1 lime" or a similar measurement. Often times when cooking people use bottled juices, pre-sliced vegetables and other convenient cooking time savers. Produce Converter will help you convert the "juice of 1 lime" and other similar recipe instructions into tablespoons, cups and other concrete measurements.
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?
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.
Launch of the Kaggle Data Science Wiki Our new Kaggle developer, Adam Kennedy, introduces the new Kaggle Wiki: The Kaggle Public Wiki launches today in Beta. We have built it from the ground up to support the odd mix of science, math and code that makes our sport unique. Since arriving at Kaggle, my main task has been to put together a suitable long-term home for everything the Kaggle community knows about competitive data science. The Kaggle forums are full of great nuggets of advice for competitive data scientists, but they aren't as good at organizing this information and improving it over time.
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();
Perform specified logical operation on input - Simulink Perform specified logical operation on input Library Logic and Bit Operations Description How Khan Academy is using Machine Learning to Assess Student Mastery See discussion on Hacker News and Reddit. The Khan Academy is well known for its extensive library of over 2600 video lessons. It should also be known for its rapidly-growing set of now 225 exercises — outnumbering stitches on a baseball — with close to 2 million problems done each day. To determine when a student has finished a certain exercise, we award proficiency to a user who has answered at least 10 problems in a row correctly — known as a streak. Proficiency manifests itself as a gold star, a green patch on teachers’ dashboards, a requirement for some badges (eg. gain 3 proficiencies), and a bounty of “energy” points.