Unify ecosystem.md

docs/src/ecosystem.md

@@ -4,18 +4,91 @@ One of the main strengths of Julia lies in an ecosystem of packages
 globally providing a rich and consistent user experience.
 
 This is a non-exhaustive list of Julia packages, nicely complementing `Flux` in typical
-machine learning and deep learning workflows:
-
-- [ArgParse.jl](https://github.com/carlobaldassi/ArgParse.jl): package for parsing command-line arguments to Julia programs.
-- [Augmentor.jl](https://github.com/Evizero/Augmentor.jl): a fast image augmentation library in Julia for machine learning.
-- [BSON.jl](https://github.com/JuliaIO/BSON.jl): package for working with the Binary JSON serialisation format
-- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl): in-memory tabular data in Julia
-- [DrWatson.jl](https://github.com/JuliaDynamics/DrWatson.jl):  a scientific project assistant software
-- [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl): utility package for accessing common machine learning datasets
-- [OnlineStats.jl](https://github.com/joshday/OnlineStats.jl): single-pass algorithms for statistics
-- [Parameters.jl](https://github.com/mauro3/Parameters.jl): types with default field values, keyword constructors and (un-)pack macros
-- [ProgressMeter.jl](https://github.com/timholy/ProgressMeter.jl): progress meters for long-running computations
-- [TensorBoardLogger.jl](https://github.com/PhilipVinc/TensorBoardLogger.jl): easy peasy logging to [tensorboard](https://www.tensorflow.org/tensorboard) in Julia
-- [ParameterSchedulers.jl](https://github.com/darsnack/ParameterSchedulers.jl): standard scheduling policies for machine learning
+machine learning and deep learning workflows. To add your project please send a [PR](https://github.com/FluxML/Flux.jl/pulls).
+See also academic work citing Flux or Zygote.
+
+## Advanced models
+
+- [FluxArchitectures.jl](https://github.com/sdobber/FluxArchitectures.jl) is a collection of slightly more advanced network architectures.
+
+## Computer vision
+
+- [ObjectDetector.jl](https://github.com/r3tex/ObjectDetector.jl) provides ready-to-go image analysis via YOLO.
+- [Metalhead.jl](https://github.com/FluxML/Metalhead.jl) includes many state-of-the-art computer vision models which can easily be used for transfer learning.
+- [UNet.jl](https://github.com/DhairyaLGandhi/UNet.jl) is a generic UNet implementation.
+
+## Datasets
+
+- [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) focuses on downloading, unpacking, and accessing benchmark datasets.
+
+## Differentiable programming
+
+- The [SciML](https://sciml.ai/) ecosystem uses Flux and Zygote to mix neural nets with differential equations, to get the best of black box and mechanistic modelling.
+- [DiffEqFlux.jl](https://github.com/SciML/DiffEqFlux.jl) provides tools for creating Neural Differential Equations.
+- [Flux3D.jl](https://github.com/FluxML/Flux3D.jl) shows off machine learning on 3D data.
+- [RayTracer.jl](https://github.com/avik-pal/RayTracer.jl) combines ML with computer vision via a differentiable renderer.
+- [Duckietown.jl](https://github.com/tejank10/Duckietown.jl) Differentiable Duckietown simulator.
+- The [Yao.jl](https://github.com/QuantumBFS/Yao.jl) project uses Flux and Zygote for Quantum Differentiable Programming.
+- [AtomicGraphNets.jl](https://github.com/Chemellia/AtomicGraphNets.jl) enables learning graph based models on atomic systems used in chemistry.
+- [DiffImages.jl](https://github.com/SomTambe/DiffImages.jl) differentiable computer vision modeling in Julia with the Images.jl ecosystem.
+
+## Graph learning
+
+- [GeometricFlux.jl](https://github.com/FluxML/GeometricFlux.jl) makes it easy to build fast neural networks over graphs.
+- [NeuralOperators.jl](https://github.com/SciML/NeuralOperators.jl) enables training infinite dimensional PDEs by learning a continuous function instead of using the finite element method.
+- [SeaPearl.jl](https://github.com/corail-research/SeaPearl.jl) is a Constraint Programming solver that uses Reinforcement Learning based on graphs as input.
+
+## Natural language processing
+
+- [Transformers.jl](https://github.com/chengchingwen/Transformers.jl) provides components for Transformer models for NLP, as well as providing several trained models out of the box.
+- [TextAnalysis.jl](https://github.com/JuliaText/TextAnalysis.jl) provides several NLP algorithms that use Flux models under the hood.
+
+## Pipeline extensions
+
+- [DLPipelines.jl](https://github.com/lorenzoh/DLPipelines.jl) is an interface for defining deep learning data pipelines.
+
+## Plumbing
+
+Tools to put data into the right order for creating a model.
+
+- [Augmentor.jl](https://github.com/Evizero/Augmentor.jl) is a real-time library augmentation library for increasing the number of training images.
+- [DataAugmentation.jl](https://github.com/lorenzoh/DataAugmentation.jl) aims to make it easy to build stochastic label-preserving augmentation pipelines for your datasets.
+- [MLDataUtils.jl](https://github.com/JuliaML/MLDataUtils.jl) is a utility for generating, loading, partitioning, and processing Machine Learning datasets.
+- [MLLabelUtils.j](https://github.com/JuliaML/MLLabelUtils.jl) is a utility for working with classification targets. It provides the necessary functionality for interpreting class-predictions, as well as converting classification targets from one encoding to another. 
+
+## Probabilistic programming
+
+- [Turing.jl](https://github.com/TuringLang/Turing.jl) extends Flux's differentiable programming capabilities to probabilistic programming.
+- [Omega.jl](https://github.com/zenna/Omega.jl) is a research project aimed at causal, higher-order probabilistic programming.
+- [Stheno.jl](https://github.com/willtebbutt/Stheno.jl) provides flexible Gaussian processes.
+
+## Reinforcement learning
+
+- [AlphaZero.jl](https://github.com/jonathan-laurent/AlphaZero.jl) provides a generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
+- [ReinforcementLearning.jl](https://juliareinforcementlearning.org/) offers a collection of tools for doing reinforcement learning research in Julia.
+
+## Parameters
+
+- [Parameters.jl](https://github.com/mauro3/Parameters.jl) types with default field values, keyword constructors and (un-)pack macros.
+- [ParameterSchedulers.jl](https://github.com/darsnack/ParameterSchedulers.jl) standard scheduling policies for machine learning.
+
+## Statistics
+
+- [OnlineStats.jl](https://github.com/joshday/OnlineStats.jl) provides single-pass algorithms for statistics.
+
+## Miscellaneous
+
+- [AdversarialPrediction.jl](https://github.com/rizalzaf/AdversarialPrediction.jl) provides a way to easily optimize generic performance metrics in supervised learning settings using the [Adversarial Prediction](https://arxiv.org/abs/1812.07526) framework.
+- [Mill.jl](https://github.com/CTUAvastLab/Mill.jl) helps to prototype flexible multi-instance learning models.
+- [MLMetrics.jl](https://github.com/JuliaML/MLMetrics.jl) is a utility for scoring models in data science and machine learning.
+- [Torch.jl](https://github.com/FluxML/Torch.jl) exposes torch in Julia.
+- [ValueHistories.jl](https://github.com/JuliaML/ValueHistories.jl) is a utility for efficient tracking of optimization histories, training curves or other information of arbitrary types and at arbitrarily spaced sampling times.
+- [InvertibleNetworks.jl](https://github.com/slimgroup/InvertibleNetworks.jl/) Building blocks for invertible neural networks in the Julia programming language.
+- [ProgressMeter.jl](https://github.com/timholy/ProgressMeter.jl) progress meters for long-running computations.
+- [TensorBoardLogger.jl](https://github.com/PhilipVinc/TensorBoardLogger.jl) easy peasy logging to [tensorboard](https://www.tensorflow.org/tensorboard) in Julia
+- [ArgParse.jl](https://github.com/carlobaldassi/ArgParse.jl) is a package for parsing command-line arguments to Julia programs.
+- [BSON.jl](https://github.com/JuliaIO/BSON.jl) is a package for working with the Binary JSON serialisation format.
+- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) in-memory tabular data in Julia.
+- [DrWatson.jl](https://github.com/JuliaDynamics/DrWatson.jl) is a scientific project assistant software.
 
 This tight integration among Julia packages is shown in some of the examples in the [model-zoo](https://github.com/FluxML/model-zoo) repository.