Page Index - SoojungHong/MachineLearning GitHub Wiki

53 page(s) in this GitHub Wiki:

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