Table of Contents

1. ML_COURSEBARI
   • Authors
   • Requirements
   • Instructions
   • Your own Neural Network from scratch

ML_COURSEBARI

The NormDat_test.txt and NormDat_train.txt files contain 30.000 randomized and normalized examples, each having the first column with the true label and 23 particle physics features. This is used by a ML class in Bari for testing different Machine Learning algorithms and their performance

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Authors

• Brunella D'Anzi (University and INFN Bari)
• Francesco Sivo (University and INFN Bari)

Requirements

$ pip install numpy
$ pip install sklearn
$ pip install matplotlib
$ pip install pandas

Instructions

In the ML_COURSEBARI\ directory of this repository:

$ python ml_exercise.py

The command will produce .png files:

• ROC curves ROC_results.png
• Loss function vs Trainsize numbertrainingsamples_loss_mse.png
• MLP score vs Trainsize numbertrainingsamples_MLP_score.png

Examples of the plot produced are stored in the Plots_NNtraining\ directory for the usage of a Multi Layer Perceptron (MLP) and a Boosted Decision Tree (BDT) implemented by using scikit-learn package.

Your own Neural Network from scratch

$ python NN_backpropagation_bdanzi.py

This python code has been written to implement the backpropagation process in Neural Network training not using already available open-source packages. The command will produce the .png file:

• Loss function vs Epochs This code represents a home-made (no usage of numpy, scikit-learn,keras) Neural Network having one hidden layer.