updated readme and some compat stuff

Project.toml

@@ -23,12 +23,14 @@ cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 [compat]
 Compat = "4.7.0"
 ComputationalResources = "0.3.2"
+cuDNN = "1"
 Flux = "0.12, 0.13, 0.14"
 MLJFlux = "0.2.10, 0.3, 0.4"
 MLJModelInterface = "1.8.0"
 MLUtils = "0.4.3"
 Parameters = "0.12"
 ProgressMeter = "1.7.2"
+Statistics = "1"
 Tables = "1.10.1"
 Tullio = "0.3.5"
 Zygote = "0.6"

README.md

@@ -2,7 +2,7 @@
 
 ![](docs/src/assets/wide_logo.png)
 
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/dev) [![Build Status](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/JuliaTrustworthyAI/LaplaceRedux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaTrustworthyAI/LaplaceRedux.jl) [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) [![ColPrac: Contributor’s Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet.png)](https://github.com/SciML/ColPrac) [![Twitter Badge](https://img.shields.io/twitter/url/https/twitter.com/paltmey.svg?style=social&label=Follow%20%40paltmey)](https://twitter.com/paltmey)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/dev) [![Build Status](https://github.com/juliatrustworthyai/LaplaceRedux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/juliatrustworthyai/LaplaceRedux.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/juliatrustworthyai/LaplaceRedux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/juliatrustworthyai/LaplaceRedux.jl) [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) [![License](https://img.shields.io/github/license/juliatrustworthyai/LaplaceRedux.jl)](LICENSE) [![Package Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/LaplaceRedux/.png)](https://pkgs.genieframework.com?packages=LaplaceRedux)
 
 `LaplaceRedux.jl` is a library written in pure Julia that can be used for effortless Bayesian Deep Learning through Laplace Approximation (LA). In the development of this package I have drawn inspiration from this Python [library](https://aleximmer.github.io/Laplace/index.html#setup) and its companion [paper](https://arxiv.org/abs/2106.14806) (Daxberger et al. 2021).
 
@@ -22,13 +22,17 @@ using Pkg
 Pkg.add("https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl")
 ```
 
+## 🏃 Getting Started
+
+If you are new to Deep Learning in Julia or simply prefer learning through videos, check out this awesome YouTube [tutorial](https://www.youtube.com/channel/UCQwQVlIkbalDzmMnr-0tRhw) by [doggo.jl](https://www.youtube.com/@doggodotjl/about) 🐶. Additionally, you can also find a [video](https://www.youtube.com/watch?v=oWko8FRj_64) of my presentation at JuliaCon 2022 on YouTube.
+
 ## 🖥️ Basic Usage
 
 `LaplaceRedux.jl` can be used for any neural network trained in [`Flux.jl`](https://fluxml.ai/Flux.jl/dev/). Below we show basic usage examples involving two simple models for a regression and a classification task, respectively.
 
 ### Regression
 
-A complete worked example for a regression model can be found in the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/regression/). Here we jump straight to Laplace Approximation and take the pre-trained model `nn` as given. Then LA can be implemented as follows, where we specify the model `likelihood`. The plot show the fitted values overlaid with a 95% confidence interval. As expected, predictive uncertainty quickly increases in areas that are not populated by any training data.
+A complete worked example for a regression model can be found in the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/regression/). Here we jump straight to Laplace Approximation and take the pre-trained model `nn` as given. Then LA can be implemented as follows, where we specify the model `likelihood`. The plot shows the fitted values overlaid with a 95% confidence interval. As expected, predictive uncertainty quickly increases in areas that are not populated by any training data.
 
 ``` julia
 la = Laplace(nn; likelihood=:regression)
@@ -44,12 +48,10 @@ plot(la, X, y; zoom=-5, size=(500,500))
 Once again we jump straight to LA and refer to the [docs](https://www.paltmeyer.com/LaplaceRedux.jl/dev/tutorials/mlp/) for a complete worked example involving binary classification. In this case we need to specify `likelihood=:classification`. The plot below shows the resulting posterior predictive distributions as contours in the two-dimensional feature space: note how the **Plugin** Approximation on the left compares to the Laplace Approximation on the right.
 
 ``` julia
-theme(:lime)
-
 la = Laplace(nn; likelihood=:classification)
 fit!(la, data)
 la_untuned = deepcopy(la)   # saving for plotting
-optimize_prior!(la; n_steps=500)
+optimize_prior!(la; n_steps=100)
 
 # Plot the posterior predictive distribution:
 zoom=0

README.qmd

@@ -17,12 +17,6 @@ jupyter: julia-1.9
 
 ![](docs/src/assets/wide_logo.png)
 
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/dev)
-[![Build Status](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/actions/workflows/CI.yml?query=branch%3Amain)
-[![Coverage](https://codecov.io/gh/JuliaTrustworthyAI/LaplaceRedux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaTrustworthyAI/LaplaceRedux.jl)
-[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
-[![Twitter Badge](https://img.shields.io/twitter/url/https/twitter.com/paltmey.svg?style=social&label=Follow%20%40paltmey)](https://twitter.com/paltmey)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/LaplaceRedux.jl/dev) [![Build Status](https://github.com/juliatrustworthyai/LaplaceRedux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/juliatrustworthyai/LaplaceRedux.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/juliatrustworthyai/LaplaceRedux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/juliatrustworthyai/LaplaceRedux.jl) [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) [![License](https://img.shields.io/github/license/juliatrustworthyai/LaplaceRedux.jl)](LICENSE) [![Package Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/LaplaceRedux/)](https://pkgs.genieframework.com?packages=LaplaceRedux) 
 
 {{< include docs/src/_intro.qmd >}}