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fys-stk-project2's Introduction

Classification and Regression, from linear and logistic regression to neural networks

This repository contains programs, test runs, material and report for project 2 in FYS-STK4155 made in collaboration between Oda Hovet (odasho), Ilse Kuperus (ilseku) and Erik Alexander Sandvik (erikasan).

Structure

Report folder

  • Contains the PDF of the report

Programs folder

  • mylearn package with logistic regression and linear regression with optimization methods for gradient descent
  • Code for neural network: neural_network.py
  • Code for classification with neural network: classification_with_neural_network.py
  • Notebook for gradient descent regression: GD_Regression.ipynb
  • Code for loading MNIST data set: mnist_loader.py

Test runs folder

  • The mylearn package to run the other programs.
  • Tests for logistic regression: LogisticRegression.ipynb
  • Tests for verifying gradient descent optimization for linear regression: verify_GDRegressor.ipynb
  • Test runs for regression with neural network: regression_with_neural_network.py

Figures folder

  • Contains figures used in the report

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