Contents

Contents:

• About daal4py
  • Designed for Data Scientists and Framework Designers
  • API Design and usage
  • oneAPI and GPU support in daal4py
  • Daal4py’s Design
  • Built for Performance
  • Getting daal4py
  • Supported configurations
  • Overview
  • Scikit-Learn API and patching
• Data
  • Numpy Arrays
  • Pandas DataFrames
  • SciPy Sparse CSR Matrix
  • CSV Files
• Supported Algorithms
  • Classification
  • Regression
  • Principal Component Analysis (PCA)
  • K-Means Clustering
  • Density-Based Spatial Clustering of Applications with Noise
  • Outlier Detection
  • Optimization Solvers
  • Distances
  • Expectation-Maximization (EM)
  • QR Decomposition
  • Normalization
  • Random Number Engines
  • Distributions
  • Association Rules
  • Cholesky Decomposition
  • Correlation and Variance-Covariance Matrices
  • Implicit Alternating Least Squares (implicit ALS)
  • Moments of Low Order
  • Quantiles
  • Singular Value Decomposition (SVD)
  • Sorting
  • Trees
• Distribution
  • It’s Easy
  • Supported Algorithms and Examples
• Streaming
  • Supported Algorithms and Examples
• Examples
  • Data Science examples
  • General usage
• Scikit-Learn API
  • oneDAL accelerated scikit-learn
  • scikit-learn verbose
  • scikit-learn API