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bat · Dec 16, 2023 · 99431c7 · 99431c7
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diff --git a/Project.toml b/Project.toml
@@ -15,6 +15,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 MonotonicSplines = "568f7cb4-8305-41bc-b90d-d32b39cc99d1"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"

diff --git a/docs/src/index.md b/docs/src/index.md
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 # AdaptiveFlows.jl
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+AdaptiveFlows.jl provides a framework for working with Normalizing Flows, a method from machine learning that transforms complex probability distributions into simpler ones, often a $D$-dimensional standard Normal distribution. This transformation is achieved through a series of invertible and differentiable mappings.
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+The package is particularly useful when dealing with real-world data that follow challenging probability distributions, making traditional statistical methods difficult or impossible to apply. By shifting the focus from the complex distribution itself to finding a transformation from it to a simple distribution, Normalizing Flows can alleviate these difficulties.
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+This package offers functionality for both forward and inverse transformations. This means that not only can it transform samples drawn from a complex distribution so that the transformed samples follow a simple distribution, but it can also transform samples drawn from a simple distribution so that the transformed samples follow a complex target distribution.
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+With these functionalities, AdaptiveFlows.jl provides tools for density evaluation and sampling of a target distribution, making it a versatile tool for tasks such as clustering and classification, density estimation, and variational inference.