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+# The Julia Ecosystem
+
+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/joshday/OnlineStats.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
+- [ProgressMeters.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