New Materials for PyDataIt

leriomaggio · Apr 8, 2017 · 9a2914d · 9a2914d
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diff --git a/0. Preamble.ipynb b/0. Preamble.ipynb
diff --git a/1.1 Introduction - Deep Learning and ANN.ipynb b/1.1 Introduction - Deep Learning and ANN.ipynb
diff --git a/1.1.1 Perceptron and Adaline.ipynb b/1.1.1 Perceptron and Adaline.ipynb
diff --git a/1.1.2 MLP and MNIST.ipynb b/1.1.2 MLP and MNIST.ipynb
diff --git a/1.2 Introduction - Tensorflow.ipynb b/1.2 Introduction - Tensorflow.ipynb
diff --git a/1.3 Introduction - Keras.ipynb b/1.3 Introduction - Keras.ipynb
diff --git a/1.4 Keras Backend.ipynb b/1.4 Keras Backend.ipynb
diff --git a/1.5.1 (Extra) Introduction - Theano.ipynb b/1.5.1 (Extra) Introduction - Theano.ipynb
diff --git a/2.1 Supervised Learning - ConvNets.ipynb b/2.1 Supervised Learning - ConvNets.ipynb
diff --git a/2.2.1 ConvNet HandsOn Part I.ipynb b/2.2.1 ConvNet HandsOn Part I.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# ConvNet HandsOn with Keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Problem Definition\n",
+    "\n",
+    "*Recognize handwritten digits*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Data\n",
+    "\n",
+    "The MNIST database ([link](http://yann.lecun.com/exdb/mnist)) has a database of handwritten digits. \n",
+    "\n",
+    "The training set has $60,000$ samples. \n",
+    "The test set has $10,000$ samples.\n",
+    "\n",
+    "The digits are size-normalized and centered in a fixed-size image. \n",
+    "\n",
+    "The data page has description on how the data was collected. It also has reports the benchmark of various algorithms on the test dataset. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Load the data\n",
+    "\n",
+    "The data is available in the repo's `data` folder. Let's load that using the `keras` library. \n",
+    "\n",
+    "For now, let's load the data and see how it looks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import keras\n",
+    "from keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [],
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the datasets\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Basic data analysis on the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# What is the type of X_train?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# What is the type of y_train?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find number of observations in training data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find number of observations in test data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Display first 2 records of X_train\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Display the first 10 records of y_train\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find the number of observations for each digit in the y_train dataset \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find the number of observations for each digit in the y_test dataset \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# What is the dimension of X_train?. What does that mean?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Display Images\n",
+    "\n",
+    "Let's now display some of the images and see how they look\n",
+    "\n",
+    "We will be using `matplotlib` library for displaying the image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot\n",
+    "import matplotlib as mpl\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Displaying the first training data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "fig = pyplot.figure()\n",
+    "ax = fig.add_subplot(1,1,1)\n",
+    "imgplot = ax.imshow(X_train[0], cmap=mpl.cm.Greys)\n",
+    "imgplot.set_interpolation('nearest')\n",
+    "ax.xaxis.set_ticks_position('top')\n",
+    "ax.yaxis.set_ticks_position('left')\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Let's now display the 11th record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}