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  • Welcome
  • Guide
    • Quick Start
    • Fitting a Line
    • Gradients and Layers
    • Training
    • Recurrence
    • GPU Support
    • Saving & Loading
    • Performance Tips
  • Ecosystem
  • Reference
    • Built-in Layers
    • Activation Functions
    • Weight Initialisation
    • Loss Functions
    • Training API
    • Optimisation Rules
    • Shape Inference
    • Flat vs. Nested
    • Callback Helpers
    • Gradients – Zygote.jl
    • Gradients – Enzyme.jl
    • Transfer Data to GPU – MLDataDevices.jl
    • Batching Data – MLUtils.jl
    • OneHotArrays.jl
    • Low-level Operations – NNlib.jl
    • Nested Structures – Functors.jl
  • Tutorials
    • Linear Regression
    • Logistic Regression
    • Custom Layers
    • Model Zoo
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  • Tutorials
  • Model Zoo
  • Model Zoo
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Model Zoo

The model zoo is a collection of examples that demonstrate how to build and train models using Flux. The examples are organised by domain and include vision, text, and audio. Each example includes a description of the model, the data used, and the training process.

Some of the examples are pedagogical, see for instance

  • Multilayer Perceptron
  • Simple Convolutional Neural Network

Others are more advanced, see for instance

  • Variational Autoencoder
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