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Machine Learning Models (Nueral Networks (Weight Initialization (All Zero…
Machine Learning Models
Regression
Linear Regression
Generalised Linear Models (GLMs)
Locally Estimated Scatterplot Smoothing (LOESS)
Ridge Regression
Least Absolute Shrinkage and Selection Operator (LASSO)
Logistic Regression
Logistic Function
Bayesian
Naive Bayes
Naive Bayes Classifier
Multinominal Naive Bayes
Bayesian Belief Network (BBN)
Dimensionality Reduction
Principal Component Analysis (PCA)
Partial Least Squares Regression (PLSR)
Partial Least Squares Regression (PLSR)
Partial Least Squares Discriminant Analysis
Quadratic Discriminant Analysis (QDA)
Linear Discriminant Analysis (LDA)
Instance Based
k-nearest Neighbour (kNN)
Learning Vecor Quantization (LVQ)
Self-Organising Map (SOM)
Locally Weighted Learning (LWL)
Decision Tree
Random Forest
Classification and Regression Tree (CART)
Gradient Boosting Machines (GBM)
Condistional Decision Trees
Gradient Boosted Regression Trees (GBRT)
Clustering
Algorithms
Hierarchical Clustering
Linkage
Complete
Single
Average
Centroid
Dissimilarity Measure
Euclidean
Manhattan
k-Means
How many clusters do we select?
k-Medians
Fuzzy C-Means
Self-Organising Maps (SOM)
Expectation Maximization
DBScan
Validation
Data Structure Metrics
Dunn Index
Connectivity
Silhouette Width
Stability Metrics
Non-overlap APN
Average Distance AO
Average Distance Between Means ADM
Figure of Merit FOM
Nueral Networks
Unit (Neurons)
Inut Layer
Hidden Layers
Batch Normalization
Learning Rate
Weight Initialization
All Zero Initialization
Linitialization with Smal Random Numbers
Calibrating the Variances
Backpropogation
Activation Functions
Defines the output of that node giveen ian input or set of inputs
Types
ReLU
Sigmoid/Logistic
Binary
Tanh
Softplus
Softmax
Maxout