Tutorials: Notebooks 📔

We propose here several tutorials to discover the different functionalities that the library has to offer.

We decided to host those tutorials on Google Colab mainly because you will be able to play the notebooks with a GPU which should greatly improve your User eXperience.

Here is the lists of the available tutorial for now:

Getting Started

Tutorial Name	Notebook
Getting Started
Sanity checks for Saliency Maps
Times Series and Regression
Tabular data and Regression
Object detection
Semantic Segmentation
Metrics
Concept Activation Vectors
Feature Visualization
Example-Based Methods
Prototypes

Attributions

Category	Tutorial Name	Notebook
BlackBox	KernelShap
BlackBox	Lime
BlackBox	Occlusion
BlackBox	Rise
WhiteBox	DeconvNet
WhiteBox	FEM (Feature Explanation Method)
WhiteBox	GradCAM
WhiteBox	GradCAM++
WhiteBox	GradientInput
WhiteBox	GuidedBackpropagation
WhiteBox	IntegratedGradients
WhiteBox	Saliency
WhiteBox	SmoothGrad
WhiteBox	SquareGrad
WhiteBox	VarGrad
Tabular Data	Regression Tabular Data
Times Series	Regression Time Series

Metrics

Category	Tutorial Name	Notebook
Fidelity	MuFidelity
Fidelity	Insertion
Fidelity	Deletion
Fidelity	Average Drop/Increase/Gain
Complexity	Complexity
Randomization	Randomization
Stability	AverageStability	(WIP)

PyTorch Wrapper

Tutorial Name	Notebook
PyTorch models: Getting started
Metrics: With PyTorch models
Object detection on PyTorch model
Semantic Segmentation on PyTorch model

Concepts extraction

WIP

Feature Visualization

Tutorial Name	Notebook
Feature Visualization: Getting started
Modern Feature Visualization: MaCo

Example-Based Methods

Tutorial Name	Notebook
Example-Based Methods: Getting started
Example-based: Prototypes