PPT-Neural Networks for Machine Learning

Lecture 14a Learning layers of features by stacking RBMs Training a deep network by stacking RBMs First train a layer of features that receive input directly from the pixels Then treat the activations of the trained features as if they were pixels and learn features of features in a second hidden layer. Easy to understand Easy to code by hand Often used to represent inputs to a net Easy to learn This is what mixture models do Each cluster corresponds to one neuron Easy to associate with other representations or responses But localist models are ver 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. Clustering and pattern recognition. W. ikipedia entry on machine learning. 7.1 Decision tree learning. 7.2 Association rule learning. 7.3 Artificial neural networks. 7.4 Genetic programming. 7.5 Inductive logic programming. Completely Different. (again). Software Defined Intelligence. A New Interdisciplinary Approach to Intelligent Infrastructure. David Meyer. Networking Field Day 8. http://techfieldday.com/event/nfd8/. Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Chong Ho Yu. What is data mining?. Data mining (DM) is a cluster of techniques, including decision trees, artificial neural networks, and clustering, which has been employed in the field Business Intelligence (BI) for years.. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Omid Kashefi. omid.Kashefi@pitt.edu. Visual Languages Seminar. November, 2016. Outline. Machine Translation. Deep Learning. Neural Machine Translation. Machine Translation. Machine Translation. Use of software in translating from one language into another. OPPORTUNITIES AND PITFALLS. What I’m going to talk about. Extremely broad topic – will keep it high level. Why and how you might use ML. Common pitfalls – not ‘classic’ data science. Some example applications and algorithms that I like. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand