{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##

✅ Put your name here

\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 19 Pre-class Assignment: Perceptrons \n", "\n", "\n", "## Goals for today's pre-class assignment\n", "\n", "

\n", "\n", "1. What is the basis of a neural network\n", "1. Understand one type of early neural network, the perceptron \n", "\n", "## Assignment instructions\n", "\n", "**This assignment is due by 11:59 p.m. the day before class** and should be uploaded into the appropriate \"Pre-class assignments\" submission folder in the Desire2Learn website. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Neural Networks\n", "\n", "If you are talking about artificial intelligence these days, you are talking (in one way or another) about neural networks. As the name implies, neural are a large network of simple processing elements, as inspired by the neural architecture of the brain. Here is a little video to talk a bit about what a neural network is" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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\n", "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "YouTubeVideo(\"bfmFfD2RIcg\", width=640, height=360)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question** Given that video, what similarities do you see between a neural network and a brain? What about differences?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✎ Similarities:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. An introduction to the Perceptron model\n", "\n", "The following video will introduce you to the idea of the first kind of neural network ever developed, the perceptron. It is important to pay attention to this video as we will be implementing a perceptron during our next in-class assignment. \n", "\n", "**Watch the video and answer the question.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "YouTubeVideo(\"4Gac5I64LM4\",width=640,height=360)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question** How is this approach similar to other classifiers we have used?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✎" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "## 3. Assignment wrap-up\n", "\n", "\n", "Please fill out the form that appears when you run the code below. **You must completely fill this out in order to receive credit for the assignment!**\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import HTML\n", "HTML(\n", "\"\"\"\n", "\n", "\"\"\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---------\n", "### Congratulations, you're done with your pre-class assignment!\n", "\n", "Now, you just need to submit this assignment by uploading it to the course Desire2Learn web page for today's submission folder (Don't forget to add your names in the first cell).\n", "\n", "\n", "© Copyright Michigan State University Board of Trustees" ] } ], "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }