{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "align_type": "Left", "slide_type": "-" } }, "source": [ "### Machine Learning\n", "# Deep Learning Neural Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Luis Martí](http://lmarti.com)\n", "#### [Instituto de Computação](http://www.ic.uff)\n", "#### [Universidade Federal Fluminense](http://www.uff.br)\n", "$\\newcommand{\\vec}[1]{\\boldsymbol{#1}}$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import random\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import seaborn\n", "seaborn.set(style='whitegrid')\n", "seaborn.set_context('talk')\n", "\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "from ipywidgets import interact, interactive, fixed\n", "import ipywidgets as widgets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# tikzmagic extesion for figures - https://github.com/mkrphys/ipython-tikzmagic\n", "%load_ext tikzmagic" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# fixing a seed for reproducibility, do not do this in real life. \n", "random.seed(a=42) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "### About the notebook/slides\n", "\n", "* The slides are _programmed_ as a [Jupyter](http://jupyter.org)/[IPython](https://ipython.org/) notebook.\n", "* **Feel free to try them and experiment on your own by launching the notebooks.**\n", "\n", "* You can run the notebook online: [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/lmarti/machine-learning)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "If you are using [nbviewer](http://nbviewer.jupyter.org) you can change to slides mode by clicking on the icon:\n", "\n", "Software | Version |
---|---|
Python | 3.6.1 64bit [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)] |
IPython | 6.0.0 |
OS | Darwin 16.6.0 x86_64 i386 64bit |
scipy | 0.19.0 |
numpy | 1.12.1 |
matplotlib | 2.0.2 |
sklearn | 0.18.1 |
Tue May 23 12:07:12 2017 -03 |