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+\addtogroup tutorial_ml
+
+
+## Table of contents
+- [Basic TMVA tutorials](\ref basic)
+   - [Training](\ref training)
+   - [Applications](\ref application)
+   - [Others](\ref other)
+- [Cross validation](\ref cross_val)
+- [New TMVA interfaces](\ref new_interface)
+   - [RBDT](\ref rbdt)
+- [Deep learning in TMVA](\ref deep_learing)
+- [TMVA Keras tutorials](\ref keras)
+- [TMVA PyTorch tutorials](\ref pytorch)
+- [Inference with SOFIE](\ref inference)         
+- [Data loading for training](\ref data_loading)
+
+
+\anchor basic
+## Basic TMVA tutorials
+
+
+\anchor training
+### Training
+
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| TMVAMinimalClassification.C |  Minimal self-contained example for setting up TMVA with binary classification. |
+| TMVAMulticlass.C | Training and testing of the TMVA multiclass classification. |
+| TMVAClassification.C |  Training and testing of the TMVA classifiers. |
+| TMVAClassificationCategory.C | Training and testing of the TMVA classifiers in categorisation mode. |
+| TMVARegression.C | Training and testing of the TMVA classifiers. |
+| classification.C | |
+
+\anchor application
+### Applications
+
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| TMVAMulticlassApplication.C | Using the trained multiclass classifiers within an analysis module. |
+| TMVAClassificationApplication.C | Using the trained classifiers within an analysis module. |
+| TMVAClassificationCategoryApplication.C | Using the trained classifiers (with categories) within an analysis module. |
+| TMVACrossValidationApplication.C | Using TMVA for k-folds cross evaluation in application. |
+| TMVAMulticlassApplication.C | Using trained multiclass classifiers within an analysis module. |
+| TMVARegressionApplication.C | Using the trained regression MVAs within an analysis module. |
+
+\anchor other
+### Others
+
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| TMVAGAexample.C | Using the genetic algorithm of TMVA. |
+| TMVAGAexample2.C | Using the genetic algorithm of TMVA. |
+| TMVAMultipleBackgroundExample.C | Training of signal with three different backgrounds. |
+
+\anchor cross_val
+## Cross validation
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| TMVACrossValidation.C | Using the TMVA k-folds cross evaluation. |
+| TMVACrossValidationApplication.C | Using the TMVA k-folds cross evaluation in application. |
+| TMVACrossValidationRegression.C | Using the TMVA k-folds cross evaluation. |
+
+\anchor new_interface
+## New TMVA interfaces
+
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| createData.C | Plot the variables. |
+| tmva001_RTensor.C | Illustrate the basic features of the RTensor class, RTensor is a std::vector-like container with additional shape information. |
+| tmva002_RDataFrameAsTensor.C | Convert the content of an RDataFrame to an RTensor object. |
+| tmva003_RReader.C | Use modern interfaces models saved in TMVA XML files. |
+
+
+\anchor rbdt
+### RBDT
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| tmva100_DataPreparation.py | Prepare ROOT datasets to be nicely readable by most machine learning methods. |
+| tmva101_Training.py | Train a machine learning model with any package reading the training data directly from ROOT files. |
+| tmva102_Testing.py | Test a trained BDT model using the fast tree inference engine offered by TMVA and external tools such as scikit-learn.  |
+| tmva103_Application.C | Apply BDTs in C++ using the fast tree inference engine offered by TMVA. |
+
+\anchor deep_learing
+## Deep learning in TMVA
+
+|          **Tutorial**          || **Description** |
+|---------------|-----------------|-----------------|
+| TMVA_CNN_Classification.C | TMVA_CNN_Classification.py |  TMVA Classification example using a Convolutional Neural Network. |
+| TMVA_Higgs_Classification.C | TMVA_Higgs_Classification.py |  Classification example of TMVA based on public Higgs UCI dataset. |
+| TMVA_RNN_Classification.C | TMVA_RNN_Classification.py |  TMVA Classification example using a Recurrent Neural Network. |
+
+\anchor keras
+## TMVA Keras tutorials
+
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| ApplicationClassificationKeras.py | Apply a trained model to new data. |
+| ApplicationRegressionKeras.py |  Apply a trained model to new data (regression). | 
+| ClassificationKeras.py |  Classification in TMVA with neural networks trained with keras. |
+| GenerateModel.py |  Define and generate a keras model for use with TMVA. | 
+| MulticlassKeras.py |  Multiclass classification in TMVA with neural networks trained with keras. |
+| RegressionKeras.py | Regression in TMVA with neural networks trained with keras. |
+
+\anchor pytorch
+## TMVA PyTorch tutorials
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| ApplicationClassificationPyTorch.py | Apply a trained model to new data. |
+| ApplicationRegressionPyTorch.py |  Apply a trained model to new data (regression). |
+| ClassificationPyTorch.py | Classification in TMVA with neural networks trained with PyTorch. |
+| MulticlassPyTorch.py | Multiclass classification in TMVA with neural networks trained with PyTorch. |
+| RegressionPyTorch.py | Regression in TMVA with neural networks trained with PyTorch. |
+
+
+
+\anchor inference
+## Inference with SOFIE
+
+|          **Tutorial**          || **Description** |
+|---------------|-----------------|-----------------|
+| | TMVA_SOFIE_Inference.py | Using a trained model with Keras and make inference using SOFIE directly from Numpy. |
+| TMVA_SOFIE_Keras.C | | Parsing of Keras .h5 file into RModel object and further generating the .hxx header files for inference. |
+| TMVA_SOFIE_Keras_HiggsModel.C | | Run the SOFIE parser on the Keras model obtaining running TMVA_Higgs_Classification.C. You need to run that macro before this one. |
+| | TMVA_SOFIE_Models.py | Inference with SOFIE using a set of models trained with Keras. |
+| TMVA_SOFIE_ONNX.C | | Parsing of ONNX files into RModel object and further generating the .hxx header files for inference. |
+| TMVA_SOFIE_PyTorch.C | | Parsing of PyTorch .pt file into RModel object and further generating the .hxx header files for inference. |
+| TMVA_SOFIE_RDataFrame.C | TMVA_SOFIE_RDataFrame.py | Inference with SOFIE and RDataFrame, of a model trained with Keras. |
+| TMVA_SOFIE_RDataFrame_JIT.C | | Using a trained model with Keras and make inference using SOFIE and RDataFrame. |
+| TMVA_SOFIE_RSofieReader.C | | Using a trained model with Keras and make inference using SOFIE with the RSofieReader class. |
+
+\anchor data_loading
+## Data loading for training
+
+| **Tutorial** | **Description** |
+|--------------|-----------------|
+| RBatchGenerator_NumPy.py | Loading batches of events from a ROOT dataset as Python generators of numpy arrays. |
+| RBatchGenerator_PyTorch.py | Loading batches of events from a ROOT dataset into a basic PyTorch workflow. |
+| RBatchGenerator_TensorFlow.py | Loading batches of events from a ROOT dataset into a basic TensorFlow workflow. |
+