Chris Albon Flashcards Preview

Machine Learning > Chris Albon Flashcards > Flashcards

Flashcards in Chris Albon Flashcards Deck (300):
1

Accuracy

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2

AdaBoost

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3

Adjusted R-squared

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4

Aggomerative Clustering

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5

AIC

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6

Almost Everywhere

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7

Alpha in Ridge Regression

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8

Ansocombe's Quartet

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9

Architecture of a Neural Network

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10

Area under the curve

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11

Avoid Overfitting

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12

Back Propogation

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13

Bag of Words

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14

Bagging

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15

Bagging vs Dropout

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16

Basic Parts of Deep Learning

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17

Bayes Error

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18

Bayes Theorem

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19

Bayesian Methods Pros and Cons

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20

Bias

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21

Bias Variance Tradeoff

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22

Big O

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23

Boosting

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24

Bootstrap

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25

Brier Score

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26

C

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27

Capacity

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28

Categorical Feature

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29

Chain Rule of Calculus

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30

Chi Squared for Feature Selection

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31

Chi Squared

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32

Classification

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33

Combination

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34

Common Optimizers with Neural Networks

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35

Common Output Layer Activation Functions

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36

Concave and Convex Functions

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37

Conditional Probability

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38

Conditioning

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39

Confidence Intervals

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40

Confusion Matrix

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41

Consistency

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42

Cost and Loss Functions

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43

Cp

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44

Cross Entropy

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45

Cumulative Distribution Function

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46

Curse of Dimensionality

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47

Data Generating Distribution

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48

Dataset Augmentation

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49

DBSCAN

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50

Decision Boundary

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51

Decision Tree Regression

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52

Decision Trees

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53

Derivative

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54

Design Matrix

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55

Determinants

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56

Does k-NN learn

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57

Dot Product

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58

Downsampling

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59

Dropout

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60

Early Stopping Advantages

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61

Early Stopping

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62

Effect of One Hot Encoding on Feature Importance

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63

Eigenvector

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64

ElasticNet

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65

ELUs

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66

Encoding Ordinal Categorical Features

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67

Ensemble Methods

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68

Epoch

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69

Error Types

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70

Explained Sum of Squares

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71

Exploding Gradient Problem

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72

Extrema

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73

F-Statistic

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74

F1 Score

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75

False Positive Rate

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76

Feature Importance

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77

Feature Selection Strategies

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78

Feedforward Neural Networks

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79

Finding Linear Regression Parameters

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80

Forward Stepwise Selection

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81

Fowlkes-Mallows

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82

Frobenius Norm

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83

Function

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84

Gaussian Naive Bayes Classifier

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85

Genderalization

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86

Gini Index

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87

Grabcut

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88

Gradient Cliff

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89

Gradient Clipping

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90

Gradient Descent

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91

Gradient Descent Rule of Thumb

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92

Gradient Descent Visualized

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93

Gradient

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94

Greedy Algorithms

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95

Greek Letters 1

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96

Greek Letters 2

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97

Greek Letters 3

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98

Greek Letters 4

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99

Grid Search

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100

Hadamard Product

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101

Hamming Loss

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102

Handling Imbalanced Classes in Support Vector Machines

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103

Handling Outliers

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104

Hessian Matrix

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105

Heteroskedasticity

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106

Hidden Layer

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107

Hinge Loss

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108

How Norm Penalties Work

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109

How to Choose Hidden Unit Activation Functions

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110

Hyperparameter Tuning

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111

Hyperplane

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112

Hypothesis Space

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113

IID

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114

Imputation using k-NN

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115

Imputing Missing Values

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116

Inflection Point

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117

Initialization of Neural Network Parameters

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118

Initializing Weights in Feedforward Neural Networks

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119

Instrumental Variables

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120

Interaction Terms

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121

Interpolation

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122

Intercept Term

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123

Interquartile Range

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124

Issues with Platt Scaling

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125

Jacobian Matrix

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126

Joins

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127

K-Fold Cross Validation

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128

K-Means Clustering

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129

K-Nearest Neighbors

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130

K-Nearest Neighbors Tips and Tricks

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131

K-NN Neighborhood Size

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132

Kernel PCA

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133

Kernel Trick

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134

L1 Norm

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135

L2 Norm

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136

Lasso for Feature Selection

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137

Leaky ReLU

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138

Learning Curve

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139

Learning in Machine Learning

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140

Learning Rate

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141

Linear Activation Function

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142

Linear Combination

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143

Linear Discriminant Analysis for Dimensionality Reduction

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144

Linearly Independent

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145

Linearly Separable

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146

Log-Sum-Exp

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147

Logistic Regression

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148

Logistic Regression vs Linear Regression

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149

Logistic Sigmoid Function

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150

Manhattan Distance

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151

Matrices

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152

Matrix Inverse

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153

Matrix Multiplication

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154

Matthews Correlation Coefficient

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155

Max Norm

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156

Mean Absolute Error

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157

Mean Squared Error

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158

Meanshift Clustering by Analogy

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159

Minibatch

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160

Minimum of a Loss Function

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161

Minkowski Distance

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162

MinMax Scaling

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163

Missing at Random

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164

Missing Completely at Random

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165

Missing not at Random

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166

Model Complexity

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167

Model Identifiability

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168

Model Selection

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169

Momentum

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170

Motivation for Deep Layers

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171

Motivation for Deep Learning

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172

Multinomial Logistic Regression

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173

Natural Log

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174

Neuron

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175

No Free Lunch Theorem

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176

Noisy ReLU

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177

Non-Parametric Methods

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178

Normal Distribution

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179

Normalized Initialization of Neural Network Parameters

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180

Normalizing Observations

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181

Notation 1

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182

Notation 2

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183

Notation 3

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184

Notation 4

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185

Notation 5

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186

Notions of Probability

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187

Occams Razor

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188

Odds

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189

Odds Ratio

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190

One-Hot Encoding

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191

One Sided Label Smoothing

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192

One Vs Rest Logistic Regression

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193

Ordinary Least Squares

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194

Out of Bag Error

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195

Out of Core

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196

Outlier

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197

Overfit vs Underfit

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198

Overfitting

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199

Parameter Norm

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200

Parameter Sharing

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201

Parameters vs Hyperparameters

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202

Parametric Modeling

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203

Partial Derivative

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204

Pearsons Correlation

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205

Perceptron Learning

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206

Perceptron

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207

Platt Scaling

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208

Polynomial Regression

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209

Power Rule

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210

Precision

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211

Preprocessing Training and Test Sets

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212

Principal Component Analysis

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213

Principal Components

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214

Probability Density Function

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215

Probability Mass Function

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216

R-Squared

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217

Radius Based Nearest Neighbor Classifier

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218

Random Forest

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219

Random Variable

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220

Randomized Search

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221

Recall

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222

Receiver Operating Characteristic

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223

Regression

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224

Regularization

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225

ReLU Activation Function

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226

Residual Sum of Squares

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227

Ridge Regression

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228

RMSprop

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229

Saddle Point

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230

Saturation of the Loss Function

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231

Saturation

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232

Scalars

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233

Selecting Number of Components in PCA

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234

Sensitivity

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235

Sigmoid Activation Function

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236

Silhouette Coefficients

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237

Simpsons Paradox

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238

Slack Variable in Soft-Margin SVM

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239

Softmax Activation Function

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240

Softmax Normalization

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241

Softplus Function

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242

Sources of Uncertainty

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243

Span

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244

Sparsity

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245

Square Root

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246

Standard Deviation

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247

Standard Error of the Mean

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248

Standardization

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249

Stationary Points

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250

Stemming Words

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251

Stochastic Gradient Descent

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252

Stop Words

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253

Strategies for Highly Imbalanced Classes

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254

Strategies when you have High Variance

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255

Supervised Deep Learning Rule of Thumb

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256

Supervised vs Unsupervised

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257

Support Vector Classifier

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258

Support Vector Machine Soft-Margin Classification

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259

Support Vectors

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260

SVC Radial Basis Function Kernel

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261

T-Statistic

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262

TanH Activation Function

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263

Tensors

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264

Test Training and Validation Sets

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265

TF-IDF

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266

The Effect of Dropout on Hidden Units

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267

The Effect of Feature Scaling on Gradient Descent

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268

The Effect of Model Complexity on Training and Test Error

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269

The Random in Random Forest

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270

Therefore and Because Notation

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271

Threshold Activation

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272

Thresholding Categorical Feature Variance

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273

Tokenizing Text

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274

Tomek Link

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275

Total Sum of Squares

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276

Training and Test Error

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277

Training Error Rate

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278

True Positive Rate

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279

Typical Dropout Probabilities

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280

Underfitting

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281

Underflow

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282

Uniform Distribution

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283

Unit-Step Activation Function

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284

Upsampling

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285

Validation Curve

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286

Vanishing Gradient Problem

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287

Variance Inflation Factor

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288

Variance

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289

Variance Thresholding for Feature Selection

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290

Vectors

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291

Visualizing RSS

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292

Weak Learners

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293

Weight Decay

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294

When can we Delete Observations with Missing Values

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295

When N Equals Population

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296

Why is it Called a Cost Function

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297

Word2Vec

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298

XOR Function

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299

Youdens J Statistic

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300

Zero-One Loss

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