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Ludwig
Getting Started
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    ludwig-ai/ludwig
    ludwig-ai/ludwig
    • Ludwig
    • Getting Started
      • Installation
      • Dataset preparation
      • Training
      • Prediction and Evaluation
      • Hyperopt
      • Serving
      • Distributed training on Ray
      • Ludwig with Docker
    • User Guide
      • What is Ludwig?
      • How Ludwig Works
      • Command Line Interface
        • LudwigModel
        • Visualization
        • Supported Formats
        • Data Preprocessing
        • Data Postprocessing
        • Dataset Zoo
      • GPUs
      • Distributed Training
        • Fine-Tuning Pretrained Models
      • Hyperparameter Optimization
      • Cloud Storage
      • AutoML
      • Visualizations
      • Model Export
      • Serving
      • Third-Party Integrations
    • Configuration
      • Model Types
      • Preprocessing
        • Supported Data Types
        • Input Features (↑)
        • Output Features (↓)
        • ⇅ Binary Features
        • ⇅ Number Features
        • ⇅ Category Features
        • ⇅ Bag Features
        • ⇅ Set Features
        • ⇅ Sequence Features
        • ⇅ Text Features
        • ⇅ Vector Features
        • ↑ Audio Features
        • ↑ Date Features
        • ↑ H3 Features
        • ↑ Image Features
        • ↑ Time Series Features
      • Defaults
      • Combiner
      • Trainer
      • Hyperopt
      • Backend
    • Examples
        • Large Language Models (LLMs)
        • Text Classification
        • Tabular Data Classification
        • Image Classification
        • Multimodal Classification
        • Hyperparameter Optimization
        • GBMs in Ludwig
        • Named Entity Recognition Tagging
        • Natural Language Understanding
        • Machine Translation
        • Chit-Chat Dialogue Modeling through Sequence2Sequence
        • Sentiment Analysis
        • One-shot Learning with Siamese Networks
        • Visual Question Answering
        • Spoken Digit Speech Recognition
        • Speaker Verification
        • Binary Classification (Titanic)
        • Timeseries forecasting
        • Timeseries forecasting (Weather)
        • Movie rating prediction
        • Multi-label classification
        • Multi-Task Learning
        • Simple Regression - Fuel Efficiency Prediction
        • Fraud Detection
    • Developer Guide
      • How to Contribute
      • Codebase Structure
      • Ludwig API Guarantees
      • Add an Encoder
      • Add a Combiner
      • Add a Decoder
      • Add a Feature Type
      • Add a Metric
      • Add a Loss Function
      • Add a Tokenizer
      • Add a Hyperopt Algorithm
      • Add a Pretrained Model
      • Add an Integration
      • Add a Dataset
      • Style Guidelines and Tests
      • Unit Test Design Guidelines
      • Run Tests on GPU Using Ray
      • Release Process
    • Community
    • FAQ

    Getting Started

    Welcome to Ludwig's Getting Started Guide.

    This will go through a full workflow using the Rotten Tomatoes dataset, a CSV file with variety of feature types and a binary target:

    • Installation
    • Dataset preparation
    • Training
    • Prediction and evaluation
    • Hyperparameter optimization
    • Serving
    • Distributed training on Ray
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