Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models / Giuseppe Bonaccorso.

Format:

Book

Author/Creator:

Bonaccorso, Giuseppe, author.

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham ; Mumbai : Packt, [2018]

System Details:

text file

Summary:

Explore and master the most important algorithms for solving complex machine learning problems. About This Book Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Who This Book Is For This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide. What You Will Learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques In Detail Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems...

Contents:

Cover

Copyright and Credits

Dedication

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: Machine Learning Model Fundamentals

Models and data

Zero-centering and whitening

Training and validation sets

Cross-validation

Features of a machine learning model

Capacity of a model

Vapnik-Chervonenkis capacity

Bias of an estimator

Underfitting

Variance of an estimator

Overfitting

The Cramér-Rao bound

Loss and cost functions

Examples of cost functions

Mean squared error

Huber cost function

Hinge cost function

Categorical cross-entropy

Regularization

Ridge

Lasso

ElasticNet

Early stopping

Summary

Chapter 2: Introduction to Semi-Supervised Learning

Semi-supervised scenario

Transductive learning

Inductive learning

Semi-supervised assumptions

Smoothness assumption

Cluster assumption

Manifold assumption

Generative Gaussian mixtures

Example of a generative Gaussian mixture

Weighted log-likelihood

Contrastive pessimistic likelihood estimation

Example of contrastive pessimistic likelihood estimation

Semi-supervised Support Vector Machines (S3VM)

Example of S3VM

Transductive Support Vector Machines (TSVM)

Example of TSVM

Chapter 3: Graph-Based Semi-Supervised Learning

Label propagation

Example of label propagation

Label propagation in Scikit-Learn

Label spreading

Example of label spreading

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 locally linear embedding

Laplacian Spectral Embedding

Example of Laplacian Spectral Embedding

t-SNE

Example of t-distributed stochastic neighbor embedding

Summary.

Chapter 4: Bayesian Networks and Hidden Markov Models

Conditional probabilities and Bayes' theorem

Bayesian networks

Sampling from a Bayesian network

Direct sampling

Example of direct sampling

A gentle introduction to Markov chains

Gibbs sampling

Metropolis-Hastings sampling

Example of Metropolis-Hastings sampling

Sampling example using PyMC3

Hidden Markov Models (HMMs)

Forward-backward algorithm

Forward phase

Backward phase

HMM parameter estimation

Example of HMM training with hmmlearn

Viterbi algorithm

Finding the most likely hidden state sequence with hmmlearn

Chapter 5: EM Algorithm and Applications

MLE and MAP learning

EM algorithm

An example of parameter estimation

Gaussian mixture

An example of Gaussian Mixtures using Scikit-Learn

Factor analysis

An example of factor analysis with Scikit-Learn

Principal Component Analysis

An example of PCA with Scikit-Learn

Independent component analysis

An example of FastICA with Scikit-Learn

Addendum to HMMs

Chapter 6: Hebbian Learning and Self-Organizing Maps

Hebb's rule

Analysis of the covariance rule

Example of covariance rule application

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

Example of SOM

Chapter 7: Clustering Algorithms

k-Nearest Neighbors

KD Trees

Ball Trees

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

Fuzzy C-means

Example of fuzzy C-means with Scikit-Fuzzy

Spectral clustering

Example of spectral clustering with Scikit-Learn

Chapter 8: Ensemble Learning

Ensemble learning fundamentals

Random forests

Example of random forest with Scikit-Learn

AdaBoost

AdaBoost.SAMME

AdaBoost.SAMME.R

AdaBoost.R2

Example of AdaBoost with Scikit-Learn

Gradient boosting

Example of gradient tree boosting with Scikit-Learn

Ensembles of voting classifiers

Example of voting classifiers with Scikit-Learn

Ensemble learning as model selection

Chapter 9: Neural Networks for Machine Learning

The basic artificial neuron

Perceptron

Example of a perceptron with Scikit-Learn

Multilayer perceptrons

Activation functions

Sigmoid and hyperbolic tangent

Rectifier activation functions

Softmax

Back-propagation algorithm

Stochastic gradient descent

Weight initialization

Example of MLP with Keras

Optimization algorithms

Gradient perturbation

Momentum and Nesterov momentum

SGD with momentum in Keras

RMSProp

RMSProp with Keras

Adam

Adam with Keras

AdaGrad

AdaGrad with Keras

AdaDelta

AdaDelta with Keras

Regularization and dropout

Dropout

Example of dropout with Keras

Batch normalization

Example of batch normalization with Keras

Chapter 10: Advanced Neural Models

Deep convolutional networks

Convolutions

Bidimensional discrete convolutions

Strides and padding

Atrous convolution

Separable convolution

Transpose convolution

Pooling layers

Other useful layers

Examples of deep convolutional networks with Keras

Example of a deep convolutional network with Keras and data augmentation

Recurrent networks

Backpropagation through time (BPTT)

LSTM

GRU

Example of an LSTM network with Keras

Transfer learning

Chapter 11: Autoencoders

Autoencoders

An example of a deep convolutional autoencoder with TensorFlow.

Denoising autoencoders

An example of a denoising autoencoder with TensorFlow

Sparse autoencoders

Adding sparseness to the Fashion MNIST deep convolutional autoencoder

Variational autoencoders

An example of a variational autoencoder with TensorFlow

Chapter 12: Generative Adversarial Networks

Adversarial training

Example of DCGAN with TensorFlow

Wasserstein GAN (WGAN)

Example of WGAN with TensorFlow

Chapter 13: Deep Belief Networks

MRF

RBMs

DBNs

Example of unsupervised DBN in Python

Example of Supervised DBN with Python

Chapter 14: Introduction to Reinforcement Learning

Reinforcement Learning fundamentals

Environment

Rewards

Checkerboard environment in Python

Policy

Policy iteration

Policy iteration in the checkerboard environment

Value iteration

Value iteration in the checkerboard environment

TD(0) algorithm

TD(0) in the checkerboard environment

Chapter 15: Advanced Policy Estimation Algorithms

TD(λ) algorithm

TD(λ) 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 using a neural network

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Index.

Notes:

Description based on print version record.

ISBN:

9781788625906

1788625900

OCLC:

1042342272