Mastering Machine Learning Algorithms - Second Edition

Spis treści książki

• Why subscribe?
• About the author
• About the reviewer
• 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
• Machine Learning Model Fundamentals
  • Models and data
    • Structure and properties of the datasets
      • Limited Sample Populations
      • Scaling datasets
      • Whitening
      • Training, validation, and test sets
      • Cross-validation
  • Characteristics of a machine learning model
    • Learnability
    • Capacity of a model
      • Vapnik-Chervonenkis capacity
    • Bias of an estimator
      • Underfitting
    • Variance of an estimator
      • Overfitting
      • The Cramér-Rao bound
  • Summary
  • Further reading
• Loss Functions and Regularization
  • Defining loss and cost functions
    • Examples of cost functions
      • Mean squared error
      • Huber cost function
      • Hinge cost function
      • Categorical cross-entropy
  • Regularization
    • Examples of Regularization Techniques
      • L2 or Ridge regularization
      • L1 or Lasso regularization
      • ElasticNet
      • Early stopping
  • Summary
  • Further reading
• Introduction to Semi-Supervised Learning
  • Semi-supervised scenario
    • Causal scenarios
    • Transductive learning
    • Inductive learning
    • Semi-supervised assumptions
      • Smoothness assumption
      • Cluster assumption
      • Manifold assumption
  • Generative Gaussian Mixture
    • Generative Gaussian Mixture theory
    • Example of a Generative Gaussian Mixture
    • Generative Gaussian Mixtures summary
      • Weighted log-likelihood
  • Self-Training
    • Self-Training theory
    • Example of Self-Training with the Iris dataset
    • Self-Training summary
  • Co-Training
    • Co-Training theory
    • Example of Co-Training with the Wine dataset
    • Co-Training summary
  • Summary
  • Further reading
• Advanced Semi-Supervised Classification
  • Contrastive Pessimistic Likelihood Estimation
    • CPLE Theory
    • Example of contrastive pessimistic likelihood estimation
    • CPLE Summary
  • Semi-supervised Support Vector Machines (S3VM)
    • S3VM Theory
    • Example of S3VM
    • S3VM Summary
  • Transductive Support Vector Machines (TSVM)
    • TSVM Theory
    • Example of TSVM
      • Analysis of different TSVM configurations
    • TSVM Summary
  • Summary
  • Further reading
• Graph-Based Semi-Supervised Learning
  • Label propagation
  • Example of label propagation
    • Label propagation in scikit-learn
  • Label spreading
    • Example of label spreading
    • Increasing the smoothness with Laplacian regularization
  • Label propagation based on Markov random walks
    • Example of label propagation based on Markov random walks
  • Manifold learning
    • Isomap
      • Example of Isomap
    • Locally linear embedding
      • Example of LLE
    • Laplacian Spectral Embedding
      • Example of Laplacian Spectral Embedding
    • t-SNE
      • Example of t-distributed stochastic neighbor embedding
  • Summary
  • Further reading
• Clustering and Unsupervised Models
  • K-nearest neighbors
    • K-d trees
    • Ball trees
    • Fitting a KNN model
    • Example of KNN with scikit-learn
  • K-means
    • K-means++
    • Example of K-means with scikit-learn
  • Evaluation metrics
    • Homogeneity score
    • Completeness score
    • Adjusted Rand index
    • Silhouette score
  • Summary
  • Further reading
• Advanced Clustering and Unsupervised Models
  • Fuzzy C-means
    • Example of Fuzzy C-means with SciKit-Fuzzy
  • Spectral clustering
    • Example of spectral clustering with scikit-learn
  • DBSCAN
    • Example of DBSCAN with scikit-learn
      • The Calinski-Harabasz score
      • The Davies-Bouldin score
    • Analysis of DBSCAN results
  • Summary
  • Further reading
• Clustering and Unsupervised Models for Marketing
  • Biclustering
    • Example of Spectral Biclustering with Scikit-Learn
  • Introduction to Market Basket Analysis with the Apriori Algorithm
    • Example of Apriori in Python
  • Summary
  • Further reading
• Generalized Linear Models and Regression
  • GLMs
    • Least Squares Estimation
    • Bias and Variance of Least Squares Estimators
    • Example of Linear regression with Python
    • Computing Linear regression Confidence Intervals with Statsmodels
    • Increasing the robustness to outliers with Huber loss
  • Other regression techniques
    • Ridge Regression
      • Example of Ridge Regression with scikit-learn
    • Risk modeling with Lasso and Logistic Regression
      • Example of Risk modeling with Lasso and Logistic Regression
    • Polynomial Regression
      • Examples of Polynomial Regressions
    • Isotonic Regression
      • Example of Isotonic Regression
  • Summary
  • Further reading
• Introduction to Time-Series Analysis
  • Time-series
    • Smoothing
  • Introduction to linear models for time-series
    • Autocorrelation
    • AR, MA, and ARMA processes
      • Modeling non-stationary trend models with ARIMA
  • Summary
  • Further reading
• Bayesian Networks and Hidden Markov Models
  • Conditional probabilities and Bayes theorem
    • Conjugate priors
  • Bayesian networks
    • Sampling from a Bayesian network
      • Direct sampling
      • A gentle introduction to Markov Chains
      • Gibbs sampling
      • The Metropolis-Hastings algorithm
    • Sampling using PyMC3
      • Running the Sampling Process
    • Sampling using PyStan
  • Hidden Markov Models
    • The Forward-Backward algorithm
      • Forward phase
      • Backward phase
      • HMM parameter estimation
    • The Viterbi algorithm
      • Finding the most likely hidden state sequence using the Viterbi algorithm and hmmlearn
  • Summary
  • Further reading
• The EM Algorithm
  • MLE and MAP Learning
  • EM Algorithm
    • Convex functions and the Jensen's inequality
    • Application of the Jensen's inequality to the EM algorithm
    • An example of parameter estimation
  • Gaussian Mixture
    • Example of Gaussian Mixture with scikit-learn
    • Determining the optimal number of components using AIC and BIC
    • Automatic component selection using Bayesian Gaussian Mixture
  • Summary
  • Further reading
• Component Analysis and Dimensionality Reduction
  • Factor Analysis
    • Linear relation analysis
    • Example of Factor Analysis with scikit-learn
  • Principal Component Analysis
    • Component importance evaluation
    • Example of PCA with scikit-learn
    • Kernel PCA
    • Sparse PCA
  • Independent Component Analysis
    • Example of FastICA with scikit-learn
  • Addendum to Hidden Markov Models
  • Summary
  • Further reading
• Hebbian Learning
  • Hebb's rule
    • Analysis of the Covariance Rule
      • Example of application of the covariance rule
    • Weight vector stabilization and Oja's rule
  • Sanger's network
    • Example of Sanger's network
  • Rubner-Tavan's network
    • Example of Rubner-Tavan's Network
  • Self-organizing maps
    • Kohonen Maps
    • Example of SOM
  • Summary
  • Further reading
• Fundamentals of Ensemble Learning
  • Ensemble learning fundamentals
  • Random forests
    • Random forest fundamentals
    • Why use Decision Trees?
    • Random forests and the bias-variance trade-off
    • Example of random forest with scikit-learn
      • Feature importance
  • AdaBoost
    • AdaBoost.SAMME
    • AdaBoost.SAMME.R
    • AdaBoost.R2
    • Example of AdaBoost with scikit-learn
  • Summary
  • Further reading
• Advanced Boosting Algorithms
  • Gradient boosting
    • Loss functions for gradient boosting
    • Example of gradient tree boosting with scikit-learn
    • Example of gradient boosting with XGBoost
      • Evaluating the predictive power of the features
  • Ensembles of voting classifiers
    • Example of voting classifiers with scikit-learn
  • Ensemble learning as model selection
  • Summary
  • Further reading
• Modeling Neural Networks
  • The basic artificial neuron
  • The perceptron
    • Example of a Perceptron with scikit-learn
  • Multilayer Perceptrons (MLPs)
    • Activation functions
      • Sigmoid and Hyperbolic Tangent
      • Rectifier activation functions
      • Softmax
  • The back-propagation algorithm
    • Stochastic gradient descent (SGD)
    • Weight initialization
    • Example of MLP with TensorFlow and Keras
  • Summary
  • Further reading
• Optimizing Neural Networks
  • Optimization algorithms
    • Gradient perturbation
    • Momentum and Nesterov momentum
      • SGD with Momentum in TensorFlow and Keras
    • RMSProp
      • RMSProp in TensorFlow and Keras
    • Adam
      • Adam in TensorFlow and Keras
    • AdaGrad
      • AdaGrad with TensorFlow and Keras
    • AdaDelta
      • AdaDelta in TensorFlow and Keras
  • Regularization and Dropout
    • Regularization
      • Regularization in TensorFlow and Keras
    • Dropout
      • Dropout with TensorFlow and Keras
  • Batch normalization
    • Example of batch normalization with TensorFlow and Keras
  • Summary
  • Further reading
• Deep Convolutional Networks
  • Deep convolutional networks
  • Convolutional operators
    • Bidimensional discrete convolutions
      • Strides and Padding
    • Atrous convolution
    • Separable convolution
    • Transpose convolution
  • Pooling layers
    • Other helpful layers
  • Example of a deep convolutional network with TensorFlow and Keras
    • Example of a deep convolutional network with TensorFlow/Keras and data augmentation
  • Summary
  • Further reading
• Recurrent Neural Networks
  • Recurrent networks
    • Backpropagation through time
    • Limitations of BPTT
  • Long Short-Term Memory (LSTM)
    • Gated Recurrent Unit (GRU)
    • Example of an LSTM with TensorFlow and Keras
  • Transfer learning
  • Summary
  • Further reading
• Autoencoders
  • Autoencoders
    • Example of a deep convolutional autoencoder with TensorFlow
  • Denoising autoencoders
    • Example of a denoising autoencoder with TensorFlow
  • Sparse autoencoders
    • Adding sparseness to the Fashion MNIST deep convolutional autoencoder
  • Variational autoencoders
    • Example of a VAE with TensorFlow
  • Summary
  • Further reading
• Introduction to Generative Adversarial Networks
  • Adversarial training
  • Deep Convolutional GANs
    • Example of DCGAN with TensorFlow
    • Mode collapse
  • Wasserstein GAN
    • Example of WGAN with TensorFlow
  • Summary
  • Further reading
• Deep Belief Networks
  • Introduction to Markov random fields
  • Restricted Boltzmann Machines
    • Contrastive Divergence
  • Deep Belief Networks
    • Example of an unsupervised DBN in Python
    • Example of a supervised DBN in Python
  • Summary
  • Further reading
• Introduction to Reinforcement Learning
  • Fundamental concepts of RL
    • The Markov Decision Process
    • Environment
      • Rewards
      • A checkerboard environment in Python
    • Policy
  • Policy iteration
    • Policy iteration in the checkerboard environment
  • Value iteration
    • Value iteration in the checkerboard environment
  • The TD(0) algorithm
    • TD(0) in the checkerboard environment
  • Summary
  • Further reading
• Advanced Policy Estimation Algorithms
  • TD() algorithm
    • TD() in a more complex checkerboard environment
    • Actor-Critic TD(0) in the checkerboard environment
  • SARSA algorithm
    • SARSA in the checkerboard environment
  • Q-learning
    • Q-learning in the checkerboard environment
    • Q-learning modeling the policy with a neural network
  • Direct policy search through policy gradient
    • Example of policy gradient with OpenAI Gym Cartpole
  • Summary
  • Further reading
• Other Books You May Enjoy
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