Machine Learning Quick Reference电子书

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Machine Learning Quick Reference

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Preface

Who this book is for

What this book covers

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Conventions used

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Quantifying Learning Algorithms

Statistical models

Learning curve

Machine learning

Wright's model

Curve fitting

Residual

Statistical modeling – the two cultures of Leo Breiman

Training data development data – test data

Size of the training, development, and test set

Bias-variance trade off

Regularization

Ridge regression (L2)

Least absolute shrinkage and selection operator

Cross-validation and model selection

K-fold cross-validation

Model selection using cross-validation

0.632 rule in bootstrapping

Model evaluation

Confusion matrix

Receiver operating characteristic curve

Area under ROC

H-measure

Dimensionality reduction

Summary

Evaluating Kernel Learning

Introduction to vectors

Magnitude of the vector

Dot product

Linear separability

Hyperplanes

SVM

Support vector

Kernel trick

Kernel

Back to Kernel trick

Kernel types

Linear kernel

Polynomial kernel

Gaussian kernel

SVM example and parameter optimization through grid search

Summary

Performance in Ensemble Learning

What is ensemble learning?

Ensemble methods

Bootstrapping

Bagging

Decision tree

Tree splitting

Parameters of tree splitting

Random forest algorithm

Case study

Boosting

Gradient boosting

Parameters of gradient boosting

Summary

Training Neural Networks

Neural networks

How a neural network works

Model initialization

Loss function

Optimization

Computation in neural networks

Calculation of activation for H1

Backward propagation

Activation function

Types of activation functions

Network initialization

Backpropagation

Overfitting

Prevention of overfitting in NNs

Vanishing gradient

Overcoming vanishing gradient

Recurrent neural networks

Limitations of RNNs

Use case

Summary

Time Series Analysis

Introduction to time series analysis

White noise

Detection of white noise in a series

Random walk

Autoregression

Autocorrelation

Stationarity

Detection of stationarity

AR model

Moving average model

Autoregressive integrated moving average

Optimization of parameters

AR model

ARIMA model

Anomaly detection

Summary

Natural Language Processing

Text corpus

Sentences

Words

Bags of words

TF-IDF

Executing the count vectorizer

Executing TF-IDF in Python

Sentiment analysis

Sentiment classification

TF-IDF feature extraction

Count vectorizer bag of words feature extraction

Model building count vectorization

Topic modeling

LDA architecture

Evaluating the model

Visualizing the LDA

The Naive Bayes technique in text classification

The Bayes theorem

How the Naive Bayes classifier works

Summary

Temporal and Sequential Pattern Discovery

Association rules

Apriori algorithm

Finding association rules

Frequent pattern growth

Frequent pattern tree growth

Validation

Importing the library

Summary

Probabilistic Graphical Models

Key concepts

Bayes rule

Bayes network

Probabilities of nodes

CPT

Example of the training and test set

Summary

Selected Topics in Deep Learning

Deep neural networks

Why do we need a deep learning model?

Deep neural network notation

Forward propagation in a deep network

Parameters W and b

Forward and backward propagation

Error computation

Backward propagation

Forward propagation equation

Backward propagation equation

Parameters and hyperparameters

Bias initialization

Hyperparameters

Use case – digit recognizer

Generative adversarial networks

Hinton's Capsule network

The Capsule Network and convolutional neural networks

Summary

Causal Inference

Granger causality

F-test

Limitations

Use case

Graphical causal models

Summary

Advanced Methods

Introduction

Kernel PCA

Independent component analysis

Preprocessing for ICA

Approach

Compressed sensing

Our goal

Self-organizing maps

SOM

Bayesian multiple imputation

Summary

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