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Data science with Java : practical methods for scientists and engineers /
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boston, MA :
O'Reilly Media,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Copyright; Table of Contents; Preface; Who Should Read This Book; Why I Wrote This Book; A Word on Data Science Today; Navigating This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Data I/O; What Is Data, Anyway?; Data Models; Univariate Arrays; Multivariate Arrays; Data Objects; Matrices and Vectors; JSON; Dealing with Real Data; Nulls; Blank Spaces; Parse Errors; Outliers; Managing Data Files; Understanding File Contents First; Reading from a Text File; Reading from a JSON File; Reading from an Image File
- Writing to a Text FileMastering Database Operations; Command-Line Clients; Structured Query Language; Java Database Connectivity; Visualizing Data with Plots; Creating Simple Plots; Plotting Mixed Chart Types; Saving a Plot to a File; Chapter 2. Linear Algebra; Building Vectors and Matrices; Array Storage; Block Storage; Map Storage; Accessing Elements; Working with Submatrices; Randomization; Operating on Vectors and Matrices; Scaling; Transposing; Addition and Subtraction; Length; Distances; Multiplication; Inner Product; Outer Product; Entrywise Product; Compound Operations
- Affine TransformationMapping a Function; Decomposing Matrices; Cholesky Decomposition; LU Decomposition; QR Decomposition; Singular Value Decomposition; Eigen Decomposition; Determinant; Inverse; Solving Linear Systems; Chapter 3. Statistics; The Probabilistic Origins of Data; Probability Density; Cumulative Probability; Statistical Moments; Entropy; Continuous Distributions; Discrete Distributions; Characterizing Datasets; Calculating Moments; Descriptive Statistics; Multivariate Statistics; Covariance and Correlation; Regression; Working with Large Datasets; Accumulating Statistics
- Merging StatisticsRegression; Using Built-in Database Functions; Chapter 4. Data Operations; Transforming Text Data; Extracting Tokens from a Document; Utilizing Dictionaries; Vectorizing a Document; Scaling and Regularizing Numeric Data; Scaling Columns; Scaling Rows; Matrix Scaling Operator; Reducing Data to Principal Components; Covariance Method; SVD Method; Creating Training, Validation, and Test Sets; Index-Based Resampling; List-Based Resampling; Mini-Batches; Encoding Labels; A Generic Encoder; One-Hot Encoding; Chapter 5. Learning and Prediction; Learning Algorithms
- Iterative Learning ProcedureGradient Descent Optimizer; Evaluating Learning Processes; Minimizing a Loss Function; Minimizing the Sum of Variances; Silhouette Coefficient; Log-Likelihood; Classifier Accuracy; Unsupervised Learning; k-Means Clustering; DBSCAN; Gaussian Mixtures; Supervised Learning; Naive Bayes; Linear Models; Deep Networks; Chapter 6. Hadoop MapReduce; Hadoop Distributed File System; MapReduce Architecture; Writing MapReduce Applications; Anatomy of a MapReduce Job; Hadoop Data Types; Mappers; Reducers; The Simplicity of a JSON String as Text; Deployment Wizardry