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JAXsaw

  • Installation
  • User guide
  • Architecture tutorials
  • API tutorials
  • API reference
  • GitHub
  • Installation
  • User guide
  • Architecture tutorials
  • API tutorials
  • API reference
  • GitHub

Section Navigation

  • MLP: Titanic Classification
  • CNN: MNIST Classification
  • RNN: Seasonal Temperature Forecasting
  • LSTM: Market Volatility Forecasting
  • Transformer: Language Modeling
  • Autoencoder: MNIST Reconstruction
  • GAN: Fashion-MNIST Generation
  • Architecture tutorials

Architecture tutorials#

Build and train dense networks, convolutional networks, recurrent networks, transformers, autoencoders, and GANs. Each notebook includes the architecture, training workflow, evaluation, and saved outputs, and can be downloaded to run locally.

  • MLP: Titanic Classification
  • CNN: MNIST Classification
  • RNN: Seasonal Temperature Forecasting
  • LSTM: Market Volatility Forecasting
  • Transformer: Language Modeling
  • Autoencoder: MNIST Reconstruction
  • GAN: Fashion-MNIST Generation

Notebook downloads#

  • MLP: Titanic Classification

  • CNN: MNIST Classification

  • RNN: Seasonal Temperature Forecasting

  • LSTM: Market Volatility Forecasting

  • Transformer: Language Modeling

  • Autoencoder: MNIST Reconstruction

  • GAN: Fashion-MNIST Generation

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Compatibility and limitations

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MLP: Titanic Classification

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