Page Index - SoojungHong/MachineLearning GitHub Wiki

105 page(s) in this GitHub Wiki:

• Home
• AdaBoost vs Gradient Boosting
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• Attention
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• Autoregressive Model & Stochastic Model
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• Bagging (Boostrap Aggregating)
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• batch normalization vs. layer normalization
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• batch, epoch
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• Bayes Theorem
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• Bias and Variance trade off ( three types of error in terms of model generalization)
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• Boosting
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• CART (Classification and Regression Tree)
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• Clustering K Means vs EM (Expectation Maximization)
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• code location
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• Cross Validation
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• Data Preparation
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• Evaluating Classifier
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• Expectation
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• Good reference on Attention and Transformer
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• Gradient Descent
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• Hinge Loss
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• How to calculate parameter numbers in Neural Network
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• How to choose Kernel
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• How to resume training with saved model
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• Hyperparameter C in SVM
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• K means algorithm
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• L1 and L2 Regularization
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• l1 norm vs l2 norm
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• Lasso Regression (Least Absolute Shrinkage and Selection Operator Regression)
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• Limitation of Decision Tree
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• LSTM vs GRU comparison with good explantion
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• Machine Learning model selection cheat sheet
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• Math & ML questions
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• Matrix Factorization
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• ML questions
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• MLlib in Spark
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• NN experimental place
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• Non linear Classifiers Kernels (e.g. Regularized SVM classifier with RBF kernel)
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• Overfit vs. Underfit
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• pasting ensemble, boosting ensemble
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• Q Learning
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• Random Forest
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• Reference : Google Machine Learning learning materials (very good)
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• Reference : PyTorch
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• ReLU (Rectified Linear Unit)
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• RNN and basic understanding of Neural Network (very good)
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• significance of logistic regression coefficients if two predictors are co related?
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• Stochastic Gradient Descent
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• SVD (Singular Value Decomposition)
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• Text preprocessing in Topic Modeling
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• Topic Modelling
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• ToWatch : Fundamental about behind Deep NN
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• Vanishing Gradient Problem
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• Well defined Negative Log Likelihood
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