最新章節

• Summary
• References
• Feature unions
• Pipelines
• scikit-learn tools for machine learning architectures
• Visualization

• Summary 更新時間：2021-07-02 18:54:04
• References
• Feature unions
• Pipelines
• scikit-learn tools for machine learning architectures
• Visualization
• Modeling/Grid search/Cross-validation
• Data conversion
• Data augmentation
• Dimensionality reduction
• Normalization
• Data collection
• Machine learning architectures
• Creating a Machine Learning Architecture
• Summary
• References
• A quick glimpse inside Keras
• Image convolution
• Classification with a multi-layer perceptron
• Logistic regression
• Computing gradients
• A brief introduction to TensorFlow
• Recurrent neural networks
• Dropout layers
• Convolutional layers
• Fully connected layers
• Deep architectures
• Artificial neural networks
• Deep learning at a glance
• A Brief Introduction to Deep Learning and TensorFlow
• Summary
• References
• VADER sentiment analysis with NLTK
• Sentiment analysis
• Latent Dirichlet Allocation
• Probabilistic latent semantic analysis
• Latent semantic analysis
• Topic modeling
• Topic Modeling and Sentiment Analysis in NLP
• Summary
• References
• A sample text classifier based on the Reuters corpus
• Tf-idf vectorizing
• N-grams
• Count vectorizing
• Vectorizing
• Stemming
• Language detection
• Stopword removal
• Word tokenizing
• Sentence tokenizing
• Tokenizing
• The bag-of-words strategy
• Corpora examples
• NLTK and built-in corpora
• Introduction to Natural Language Processing
• Summary
• References
• Alternating least squares with Apache Spark MLlib
• Alternating least squares strategy
• Singular Value Decomposition strategy
• Model-based collaborative filtering
• Model-free (or memory-based) collaborative filtering
• Content-based systems
• User-based system implementation with scikit-learn
• Naive user-based systems
• Introduction to Recommendation Systems
• Summary
• References
• Connectivity constraints
• Agglomerative clustering in scikit-learn
• Dendrograms
• Agglomerative clustering
• Hierarchical strategies
• Hierarchical Clustering
• Summary
• References
• Adjusted rand index
• Completeness
• Homogeneity
• Evaluation methods based on the ground truth
• Spectral clustering
• DBSCAN
• Cluster instability
• Calinski-Harabasz index
• Silhouette score
• Optimizing the inertia
• Finding the optimal number of clusters
• K-means
• Clustering basics
• Clustering Fundamentals
• Summary
• References
• Voting classifier
• Gradient tree boosting
• AdaBoost
• Feature importance in random forests
• Random forests
• Ensemble learning
• Decision tree classification with scikit-learn
• Feature importance
• Misclassification impurity index
• Cross-entropy impurity index
• Gini impurity index
• Impurity measures
• Binary decisions
• Binary decision trees
• Decision Trees and Ensemble Learning
• Summary
• References
• Support vector regression
• Controlled support vector machines
• Non-linear examples
• Custom kernels
• Sigmoid kernel
• Polynomial kernel
• Radial Basis Function
• Kernel-based classification
• Linear classification
• scikit-learn implementation
• Linear support vector machines
• Support Vector Machines
• Summary
• References
• Gaussian naive Bayes
• Multinomial naive Bayes
• Bernoulli naive Bayes
• Naive Bayes in scikit-learn
• Naive Bayes classifiers
• Bayes' theorem
• Naive Bayes
• Summary
• ROC curve
• Classification metrics
• Finding the optimal hyperparameters through grid search
• Stochastic gradient descent algorithms
• Implementation and optimizations
• Logistic regression
• Linear classification
• Logistic Regression
• Summary
• References
• Isotonic regression
• Polynomial regression
• Robust regression with random sample consensus
• Ridge Lasso and ElasticNet
• Regressor analytic expression
• Linear regression with scikit-learn and higher dimensionality
• A bidimensional example
• Linear models
• Linear Regression
• Summary
• References
• Atom extraction and dictionary learning
• Kernel PCA
• Sparse PCA
• Non-negative matrix factorization
• Principal component analysis
• Feature selection and filtering
• Data scaling and normalization
• Managing missing features
• Managing categorical data
• Creating training and test sets
• scikit-learn toy datasets
• Feature Selection and Feature Engineering
• Summary
• References
• Elements of information theory
• Maximum-likelihood learning
• MAP learning
• Statistical learning approaches
• PAC learning
• Error measures
• Underfitting and overfitting
• Learnability
• One-vs-one
• One-vs-all
• Multiclass strategies
• Data formats
• Important Elements in Machine Learning
• Summary
• Further reading
• Machine learning and big data
• Beyond machine learning - deep learning and bio-inspired adaptive systems
• Reinforcement learning
• Unsupervised learning
• Supervised learning
• Only learning matters
• Introduction - classic and adaptive machines
• A Gentle Introduction to Machine Learning
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewers
• About the Author
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• About the Author
• About the Reviewers
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• A Gentle Introduction to Machine Learning
• Introduction - classic and adaptive machines
• Only learning matters
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Beyond machine learning - deep learning and bio-inspired adaptive systems
• Machine learning and big data
• Further reading
• Summary
• Important Elements in Machine Learning
• Data formats
• Multiclass strategies
• One-vs-all
• One-vs-one
• Learnability
• Underfitting and overfitting
• Error measures
• PAC learning
• Statistical learning approaches
• MAP learning
• Maximum-likelihood learning
• Elements of information theory
• References
• Summary
• Feature Selection and Feature Engineering
• scikit-learn toy datasets
• Creating training and test sets
• Managing categorical data
• Managing missing features
• Data scaling and normalization
• Feature selection and filtering
• Principal component analysis
• Non-negative matrix factorization
• Sparse PCA
• Kernel PCA
• Atom extraction and dictionary learning
• References
• Summary
• Linear Regression
• Linear models
• A bidimensional example
• Linear regression with scikit-learn and higher dimensionality
• Regressor analytic expression
• Ridge Lasso and ElasticNet
• Robust regression with random sample consensus
• Polynomial regression
• Isotonic regression
• References
• Summary
• Logistic Regression
• Linear classification
• Logistic regression
• Implementation and optimizations
• Stochastic gradient descent algorithms
• Finding the optimal hyperparameters through grid search
• Classification metrics
• ROC curve
• Summary
• Naive Bayes
• Bayes' theorem
• Naive Bayes classifiers
• Naive Bayes in scikit-learn
• Bernoulli naive Bayes
• Multinomial naive Bayes
• Gaussian naive Bayes
• References
• Summary
• Support Vector Machines
• Linear support vector machines
• scikit-learn implementation
• Linear classification
• Kernel-based classification
• Radial Basis Function
• Polynomial kernel
• Sigmoid kernel
• Custom kernels
• Non-linear examples
• Controlled support vector machines
• Support vector regression
• References
• Summary
• Decision Trees and Ensemble Learning
• Binary decision trees
• Binary decisions
• Impurity measures
• Gini impurity index
• Cross-entropy impurity index
• Misclassification impurity index
• Feature importance
• Decision tree classification with scikit-learn
• Ensemble learning
• Random forests
• Feature importance in random forests
• AdaBoost
• Gradient tree boosting
• Voting classifier
• References
• Summary
• Clustering Fundamentals
• Clustering basics
• K-means
• Finding the optimal number of clusters
• Optimizing the inertia
• Silhouette score
• Calinski-Harabasz index
• Cluster instability
• DBSCAN
• Spectral clustering
• Evaluation methods based on the ground truth
• Homogeneity
• Completeness
• Adjusted rand index
• References
• Summary
• Hierarchical Clustering
• Hierarchical strategies
• Agglomerative clustering
• Dendrograms
• Agglomerative clustering in scikit-learn
• Connectivity constraints
• References
• Summary
• Introduction to Recommendation Systems
• Naive user-based systems
• User-based system implementation with scikit-learn
• Content-based systems
• Model-free (or memory-based) collaborative filtering
• Model-based collaborative filtering
• Singular Value Decomposition strategy
• Alternating least squares strategy
• Alternating least squares with Apache Spark MLlib
• References
• Summary
• Introduction to Natural Language Processing
• NLTK and built-in corpora
• Corpora examples
• The bag-of-words strategy
• Tokenizing
• Sentence tokenizing
• Word tokenizing
• Stopword removal
• Language detection
• Stemming
• Vectorizing
• Count vectorizing
• N-grams
• Tf-idf vectorizing
• A sample text classifier based on the Reuters corpus
• References
• Summary
• Topic Modeling and Sentiment Analysis in NLP
• Topic modeling
• Latent semantic analysis
• Probabilistic latent semantic analysis
• Latent Dirichlet Allocation
• Sentiment analysis
• VADER sentiment analysis with NLTK
• References
• Summary
• A Brief Introduction to Deep Learning and TensorFlow
• Deep learning at a glance
• Artificial neural networks
• Deep architectures
• Fully connected layers
• Convolutional layers
• Dropout layers
• Recurrent neural networks
• A brief introduction to TensorFlow
• Computing gradients
• Logistic regression
• Classification with a multi-layer perceptron
• Image convolution
• A quick glimpse inside Keras
• References
• Summary
• Creating a Machine Learning Architecture
• Machine learning architectures
• Data collection
• Normalization
• Dimensionality reduction
• Data augmentation
• Data conversion
• Modeling/Grid search/Cross-validation
• Visualization
• scikit-learn tools for machine learning architectures
• Pipelines
• Feature unions
• References
• Summary 更新時間：2021-07-02 18:54:04