fixed links

README.md

@@ -1,9 +1,14 @@
+
 # LaplaceRedux
 
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://pat-alt.github.io/LaplaceRedux.jl/dev) [![Build Status](https://github.com/pat-alt/LaplaceRedux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/pat-alt/LaplaceRedux.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/pat-alt/LaplaceRedux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/pat-alt/LaplaceRedux.jl)
 
 This is a small library that can be used for effortless Bayesian Deep Learning and Logisitic Regression trough Laplace Approximation. It is inspired by this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806).
 
+## News 📣
+
+**JuliaCon 2022**: This project will be presented at JuliaCon 2022 in July 2022. See here for the preliminary slide pack: \[[html](https://www.paltmeyer.com/LaplaceRedux.jl/dev/resources/juliacon22/presentation.html#/title-slide)\]
+
 ## Installation
 
 This package is not registered, but can be installed from Github as follows:

docs/src/index.md

@@ -1,3 +1,4 @@
+
 ``` @meta
 CurrentModule = LaplaceRedux
 ```
@@ -8,6 +9,10 @@ Documentation for [LaplaceRedux.jl](https://github.com/pat-alt/LaplaceRedux.jl).
 
 This is a small library that can be used for effortless Bayesian Deep Learning and Logisitic Regression trough Laplace Approximation. It is inspired by this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806).
 
+## News 📣
+
+**JuliaCon 2022**: This project will be presented at JuliaCon 2022 in July 2022. See here for the preliminary slide pack: \[[html](https://www.paltmeyer.com/LaplaceRedux.jl/dev/resources/juliacon22/presentation.html#/title-slide)\]
+
 ## Installation
 
 This package is not registered, but can be installed from Github as follows:
@@ -30,6 +35,6 @@ Calling `predict(nn,X)` for some features `X` will produce posterior predictions
 
 ![](https://raw.githubusercontent.com/pat-alt/LaplaceRedux.jl/main/docs/src/tutorials/www/posterior_predictive_mlp.png)
 
-## Limitations and Goals.
+## Limitations.
 
-This library currently offers native support only for models composed and trained in Flux. It is also limited to binary classification problems.
+This library currently offers native support only for models composed and trained in Flux. It is also limited to binary classification problems. Finally, it also requires additional testing.

docs/src/intro.qmd

@@ -2,7 +2,7 @@ This is a small library that can be used for effortless Bayesian Deep Learning a
 
 ## News 📣
 
-**JuliaCon 2022**: This project will be presented at JuliaCon 2022 in July 2022. See here for the preliminary slide pack: [html](https://www.paltmeyer.com/LaplaceRedux.jl/dev/resources/juliacon22/presentation/)
+**JuliaCon 2022**: This project will be presented at JuliaCon 2022 in July 2022. See here for the preliminary slide pack: [[html](https://www.paltmeyer.com/LaplaceRedux.jl/dev/resources/juliacon22/presentation.html#/title-slide)]
 
 ## Installation
 

docs/src/resources/resources.md

@@ -5,6 +5,6 @@
 Slides: [link](juliacon22/presentation.html)
 
 ```@raw html
-<iframe style="width:560px;height:315px" src="juliacon22/presentation.html" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+<iframe style="width:560px;height:315px" src="https://www.paltmeyer.com/LaplaceRedux.jl/dev/resources/juliacon22/presentation.html" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 ```