{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Neural Networks\n", "\n", "A deep feedforward neural network consists of multiple nodes which mimic the biological neurons of a human brain. \n", "\n", "**Network:** The model is associated with a directed acyclic graph describing how the functions (layers) are composed together.\n", "\n", "**Feedforward:** The information flows from inputs to outputs through the layers of the network.\n", "\n", "**Neural:** The inspiration originated from neuroscience. Each element of a layer plays a role analogous to a neuron.\n", "\n", "The goal of a feedforward neural network is to approximate some function $\\large f^*$. For example, for a classifier, $\\large y = f^*(x)$ maps an input $\\large x$ to a category $\\large y$. A feedforward network defines a mapping $\\large y = f(x; \\theta)$ and learns the value of the parameters $\\large \\theta$ that result in the best function approximation for $\\large f^*$.\n", "\n", "![python-scistack](https://icdn5.digitaltrends.com/image/artificial_neural_network_1-791x388.jpg)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Timeline of Deep Learning\n", "\n", "Some of the biggest accomplishments and moments in deep learning:\n", "