update README file

README.md

@@ -1,4 +1,43 @@
-# AutoEncode
+# AutoEncode.jl
 
 [![Build Status](https://github.com/mrazomej/AutoEncode.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/mrazomej/AutoEncode.jl/actions/workflows/CI.yml?query=branch%3Amain)
-[![codecov](https://codecov.io/gh/mrazomej/AutoEncode.jl/graph/badge.svg?token=9DKTMW94G5)](https://codecov.io/gh/mrazomej/AutoEncode.jl)
+[![codecov](https://codecov.io/gh/mrazomej/AutoEncode.jl/graph/badge.svg?token=9DKTMW94G5)](https://codecov.io/gh/mrazomej/AutoEncode.jl)
+
+Welcome to the `AutoEncode.jl` GitHub repository. This package provides a simple
+interface for training and using [Flux.jl](https://fluxml.ai)-based autoencoders
+and variational autoencoders in Julia.
+
+## Installation
+
+You can install `AutoEncode.jl` using the Julia package manager. From the Julia
+REPL, type `]` to enter the Pkg REPL mode and run:
+
+```julia
+add AutoEncode
+```
+
+## Design
+
+The idea behind `AutoEncode.jl` is to take advantage of Julia's multiple
+dispatch to provide a simple and flexible interface for training and using
+different types of autoencoders. The package is designed to be modular and allow
+the user to easily define and test custom encoder and decoder architectures.
+Moreover, when it comes to variational autoencoders, `AutoEncode.jl` takes a
+probabilistic perspective, where the type of encoders and decoders defines (via
+multiple dispatch) the corresponding distribution used within the corresponding
+loss function.
+
+For more information, please refer to the
+[documentation](https://mrazomej.github.io/AutoEncode.jl/).
+
+## Implemented Autoencoders
+
+| model                      | module        | description                                                    |
+| -------------------------- | ------------- | -------------------------------------------------------------- |
+| Autoencoder                | `AEs`         | Vanilla deterministic autoencoder                              |
+| Variational Autoencoder    | `VAEs`        | Vanilla variational autoencoder                                |
+| β-VAE                      | `VAEs`        | beta-VAE to weigh the reconstruction vs. KL divergence in ELBO |
+| MMD-VAEs                   | `MMDs`        | Maximum-Mean Discrepancy Variational Autoencoders              |
+| InfoMax-VAEs               | `InfoMaxVAEs` | Information Maximization Variational Autoencoders              |
+| Hamiltonian VAE            | `HVAEs`       | Hamiltonian Variational Autoencoders                           |
+| Riemannian Hamiltonian-VAE | `RHVAEs`      | Riemannian-Hamiltonian Variational Autoencoder                 |