Python implementation for the assignments of the course BITS F312 ( Neural Network and Fuzzy Logic )
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Linear Regression using batch and stochastic gradient descent
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Ridge regression
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Vectorized linear regression
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Least angle regression
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K-means clustering
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Logistic regression
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Multiclass logistic regression using “One VS All” and “One VS One” multiclass coding techniques
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K-Fold cross-validation
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Likelihood ratio test (LRT)
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Maximum a posteriori (MAP) decision rule
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Maximum likelihood (ML) decision rule
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Multilayer perceptron
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Radial basis function neural network (RBFNN)
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Stacked autoencoder
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Extreme learning machine (ELM) classifier
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Deep layer stacked autoencoder based extreme learning machine
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Convolutional neural network
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Convolutional autoencoder
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Neuro-fuzzy inference system (NFIS) classifier