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Soojung Hong edited this page Feb 2, 2018 · 1 revision

Welcome to the MachineLearning wiki!

This wiki is summary of Machine Learning theory and concepts.

Toggle table of contents Pages 53

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

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