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Mastering Java for Data Science
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Summary
ThisbookisintendedforsoftwareengineerswhoarecomfortablewithdevelopingJavaapplicationsandarefamiliarwiththebasicconceptsofdatascience.Additionally,itwillalsobeusefulfordatascientistswhodonotyetknowJavabutwantorneedtolearnit.Ifyouarewillingtobuildefficientdatascienceapplicationsandbringthemintheenterpriseenvironmentwithoutchangingtheexistingstack,thisbookisforyou!
- Summary 更新時間：2021-07-02 23:44:57
- Multi-armed bandits
- A/B testing
- Online evaluation
- Search engine service
- Spring Boot
- Microservices
- Deploying Data Science Models
- Summary
- Link suggestion
- Link Prediction with MLlib and XGBoost
- Edge features
- Negative sampling
- Node features
- Extracting features from the graph
- Reading the DBLP graph
- Link prediction
- Apache Spark
- Common Crawl
- Hadoop MapReduce
- Apache Hadoop
- Scaling Data Science
- Summary
- Running DeepLearning4J on GPU
- Data augmentation
- Monitoring the performance
- Creating the model
- Reading the data
- Deep learning for cats versus dogs
- Convolutional Neural Networks
- Neural networks in DeepLearning4J
- ND4J - N-dimensional arrays for Java
- Neural Networks and DeepLearning4J
- Deep Learning with DeepLearning4J
- Summary
- XGBoost for learning to rank
- XGBoost for regression
- Feature importance
- Text features
- Parameter tuning
- XGBoost for classification
- XGBoost in practice
- Installing XGBoost
- Gradient Boosting Machines and XGBoost
- Extreme Gradient Boosting
- Summary
- Reranking with Lucene
- Learning to rank for information retrieval
- Text classification
- Supervised learning for texts
- Word embeddings
- Text clustering
- Latent Semantic Analysis
- Unsupervised learning for texts
- Machine learning for texts
- Customizing Apache Lucene
- Stanford CoreNLP
- Natural Language Processing tools
- Indexing and Apache Lucene
- Vector space model implementation
- Vector Space Model - Bag of Words and TF-IDF
- Natural Language Processing and information retrieval
- Working with Text - Natural Language Processing and Information Retrieval
- Summary
- Unsupervised Evaluation
- Supervised evaluation
- Manual evaluation
- Evaluation
- Supervised learning via clustering
- Clustering as dimensionality reduction
- Clusters as features
- Clustering for supervised learning
- DBSCAN
- Choosing K in K-Means
- K-means
- Hierarchical methods
- Cluster analysis
- Random projection
- Truncated SVD for categorical and sparse data
- Truncated SVD
- Principal Component Analysis
- Unsupervised dimensionality reduction
- Dimensionality reduction
- Unsupervised Learning - Clustering and Dimensionality Reduction
- Summary
- Case study - hardware performance
- MAE
- MSE
- Evaluation
- Other libraries
- JSAT
- Smile
- Machine learning libraries for regression
- Regression
- Case study - page prediction
- Training validation and testing
- K-fold cross-validation
- Result validation
- ROC and AU ROC (AUC)
- Precision recall and F1
- Accuracy
- Evaluation
- Encog
- LIBSVM and LIBLINEAR
- JSAT
- Smile
- Binary classification models
- Classification
- Supervised Learning - Classification and Regression
- Summary
- Joinery shell
- Interactive Java
- JVM languages
- Interactive Exploratory Data Analysis in Java
- Joinery
- Apache Commons Math
- Search engine datasets
- Exploratory data analysis in Java
- Exploratory Data Analysis
- Summary
- Search engine - preparing data
- DataFrames
- Databases
- JSON
- Web and HTML
- Text data and CSV
- Accessing data
- AOL Cyclops React
- Google Guava
- Other commons modules
- Commons Collections
- Commons IO
- Commons Lang
- Apache Commons
- Extensions to the standard library
- Streaming API
- Writing ouput data
- Reading input data
- Input/Output
- Collections
- Standard Java library
- Data Processing Toolbox
- Summary
- Text processing
- Machine learning and data mining libraries
- Math and stats libraries
- Data processing libraries
- Data science libraries
- Data science in Java
- A running example
- CRISP-DM
- Data science process models
- Natural Language Processing
- Dimensionality reduction
- Clustering
- Unsupervised learning
- Supervised learning
- Machine learning
- Data science
- Data Science Using Java
- 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
- www.PacktPub.com
- About the Reviewers
- About the Author
- Credits
- 版權信息
- 封面
- 封面
- 版權信息
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- 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
- Data Science Using Java
- Data science
- Machine learning
- Supervised learning
- Unsupervised learning
- Clustering
- Dimensionality reduction
- Natural Language Processing
- Data science process models
- CRISP-DM
- A running example
- Data science in Java
- Data science libraries
- Data processing libraries
- Math and stats libraries
- Machine learning and data mining libraries
- Text processing
- Summary
- Data Processing Toolbox
- Standard Java library
- Collections
- Input/Output
- Reading input data
- Writing ouput data
- Streaming API
- Extensions to the standard library
- Apache Commons
- Commons Lang
- Commons IO
- Commons Collections
- Other commons modules
- Google Guava
- AOL Cyclops React
- Accessing data
- Text data and CSV
- Web and HTML
- JSON
- Databases
- DataFrames
- Search engine - preparing data
- Summary
- Exploratory Data Analysis
- Exploratory data analysis in Java
- Search engine datasets
- Apache Commons Math
- Joinery
- Interactive Exploratory Data Analysis in Java
- JVM languages
- Interactive Java
- Joinery shell
- Summary
- Supervised Learning - Classification and Regression
- Classification
- Binary classification models
- Smile
- JSAT
- LIBSVM and LIBLINEAR
- Encog
- Evaluation
- Accuracy
- Precision recall and F1
- ROC and AU ROC (AUC)
- Result validation
- K-fold cross-validation
- Training validation and testing
- Case study - page prediction
- Regression
- Machine learning libraries for regression
- Smile
- JSAT
- Other libraries
- Evaluation
- MSE
- MAE
- Case study - hardware performance
- Summary
- Unsupervised Learning - Clustering and Dimensionality Reduction
- Dimensionality reduction
- Unsupervised dimensionality reduction
- Principal Component Analysis
- Truncated SVD
- Truncated SVD for categorical and sparse data
- Random projection
- Cluster analysis
- Hierarchical methods
- K-means
- Choosing K in K-Means
- DBSCAN
- Clustering for supervised learning
- Clusters as features
- Clustering as dimensionality reduction
- Supervised learning via clustering
- Evaluation
- Manual evaluation
- Supervised evaluation
- Unsupervised Evaluation
- Summary
- Working with Text - Natural Language Processing and Information Retrieval
- Natural Language Processing and information retrieval
- Vector Space Model - Bag of Words and TF-IDF
- Vector space model implementation
- Indexing and Apache Lucene
- Natural Language Processing tools
- Stanford CoreNLP
- Customizing Apache Lucene
- Machine learning for texts
- Unsupervised learning for texts
- Latent Semantic Analysis
- Text clustering
- Word embeddings
- Supervised learning for texts
- Text classification
- Learning to rank for information retrieval
- Reranking with Lucene
- Summary
- Extreme Gradient Boosting
- Gradient Boosting Machines and XGBoost
- Installing XGBoost
- XGBoost in practice
- XGBoost for classification
- Parameter tuning
- Text features
- Feature importance
- XGBoost for regression
- XGBoost for learning to rank
- Summary
- Deep Learning with DeepLearning4J
- Neural Networks and DeepLearning4J
- ND4J - N-dimensional arrays for Java
- Neural networks in DeepLearning4J
- Convolutional Neural Networks
- Deep learning for cats versus dogs
- Reading the data
- Creating the model
- Monitoring the performance
- Data augmentation
- Running DeepLearning4J on GPU
- Summary
- Scaling Data Science
- Apache Hadoop
- Hadoop MapReduce
- Common Crawl
- Apache Spark
- Link prediction
- Reading the DBLP graph
- Extracting features from the graph
- Node features
- Negative sampling
- Edge features
- Link Prediction with MLlib and XGBoost
- Link suggestion
- Summary
- Deploying Data Science Models
- Microservices
- Spring Boot
- Search engine service
- Online evaluation
- A/B testing
- Multi-armed bandits
- Summary 更新時間：2021-07-02 23:44:57
