{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A first step into data analysis" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In this python notebook you should start from the ANN build in ML_exercise_1.ipynb.\\\n", "Here you need to:\n", "1. Import a new dataset from the text file 'Data_Exercise_2.txt'\n", "2. Split the dataset into input and ouput data (3 inputs and 3 outputs)\n", "3. Use the single layer ANN built in the previous example\n", "4. Train and visualize the accuracy of the ANN\n", "5. What can you try to improve the accuracy of the ANN (tip: look at the data)?" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "For those who are using google colab you can use the following cell to open the data file remotely." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }