更新时间：2021-08-20 10:05:42
coverpage
Title Page
Copyright and Credits
Machine Learning Quick Reference
About Packt
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Packt.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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
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
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
Time Series Analysis
Introduction to time series analysis
White noise
