Feedforward neural networks - PowerPoint PPT Presentation

Data Mining - CSE5230

CSE5230/DMS/2004/5. Data Mining - CSE5230. Classifiers 1 Bayesian Classification and Bayesian Networks. Lecture Outline. What is Classification? Measuring Classifier Performance Bayesian Classifiers Bayes Theorem Naïve Bayesian Classification Example application: spam filtering

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Neural Networks

Neural Networks. Vladimir Pleskonji ć 3188/2015. General. Feedforward neural networks Inputs are numeric features Outputs are in range (0, 1). Neural network. Perceptron. Building blocks Numeric inputs Output in (0, 1) Bias (always equal to +1). Perceptron. Perceptron.

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Deep learning architectures

Deep learning architectures. Usman Roshan NJIT. Figures from https ://www.jeremyjordan.me/convnet-architectures / https:// medium.com /@sidereal/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5. Evolution of networks. LeNet.

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