deep-river

deep-river
Getting started
Benchmarks
Benchmarks
- Binary classification
  Binary classification
- Multiclass classification
  Multiclass classification
- Regression
  Regression
Examples
Examples
- Anomaly
  Anomaly
- Catastrophic forgetting
  Catastrophic forgetting
- Classification
  Classification
- Model persistence
  Model persistence
  - Index
- Regression
  Regression
Generated
Generated
- Examples
  Examples
  - Anomaly
    Anomaly
    
    Example autoencoder
    
    Example autoencoder
    
    Example lstm autoencoder
    
    Example lstm autoencoder
    
    Example probability weighted autoencoder
    
    Example probability weighted autoencoder
  - Catastrophic forgetting
    Catastrophic forgetting
    
    Label shift
    
    Label shift
    
    Recurring concepts
    
    Recurring concepts
  - Classification
    Classification
    
    Example classification
    
    Example classification
    
    Example mini batches
    
    Example mini batches
    
    Example rnn classification
    
    Example rnn classification
  - Model persistence
    Model persistence
    
    Model persistence demo
    
    Model persistence demo
  - Regression
    Regression
    
    Bike sharing forecasting
    
    Bike sharing forecasting
    
    Example mini batches
    
    Example mini batches
    
    Example regression
    
    Example regression
    
    Example rnn regression
    
    Example rnn regression
Reference
Reference
- Base
- Params
- Tensor conversion
- Anomaly
  Anomaly
- Classification
  Classification
- Regression
  Regression

API Reference¶

Use this section when you need implementation-level details: constructor parameters, method signatures, inheritance, and module-level utilities.

Start with the task-specific modules (classification, regression, and anomaly) and then move to shared foundations in base and utility helpers.