added prerequisite notebooks

README.md

@@ -5,6 +5,10 @@ It is suitable for beginners who want to find clear and concise examples about T
 
 ## Tutorial index
 
+#### 0 - Prerequisite
+- Introduction to Machine Learning ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb))
+- Introduction to MNIST Dataset ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb))
+
 #### 1 - Introduction
 - Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py))
 - Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py))
@@ -29,6 +33,12 @@ It is suitable for beginners who want to find clear and concise examples about T
 #### 5 - Multi GPU
 - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py))
 
+## Dataset
+Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py).
+MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb).
+
+Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)
+
 ## More Examples
 The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api).
 
@@ -79,7 +89,3 @@ cuda
 tflearn (if using tflearn examples)
 ```
 For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md)
-
-## Dataset
-Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py).
-MNIST is a database of handwritten digits, with 60,000 examples for training and 10,000 examples for testing. (Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/))

notebooks/0_Prerequisite/ml_introduction.ipynb

@@ -0,0 +1,47 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning\n",
+    "\n",
+    "Prior to start browsing the examples, it may be useful that you get familiar with machine learning, as TensorFlow is mostly used for machine learning tasks (especially Neural Networks). You can find below a list of useful links, that can give you the basic knowledge required for this TensorFlow Tutorial.\n",
+    "\n",
+    "## Machine Learning\n",
+    "\n",
+    "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n",
+    "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n",
+    "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n",
+    "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n",
+    "\n",
+    "## Deep Learning & Neural Networks\n",
+    "\n",
+    "- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\n",
+    "- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "IPython (Python 2.7)",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

notebooks/0_Prerequisite/mnist_dataset_intro.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# MNIST Dataset Introduction\n",
+    "\n",
+    "Most examples are using MNIST dataset of handwritten digits. It has 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image, so each sample is represented as a matrix of size 28x28 with values from 0 to 1.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "![MNIST Digits](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
+    "\n",
+    "## Usage\n",
+    "In our examples, we are using TensorFlow [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py) script to load that dataset.\n",
+    "It is quite useful for managing our data, and handle:\n",
+    "\n",
+    "- Dataset downloading\n",
+    "\n",
+    "- Loading the entire dataset into numpy array: \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Import MNIST\n",
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n",
+    "\n",
+    "# Load data\n",
+    "X_train = mnist.train.images\n",
+    "Y_train = mnist.train.labels\n",
+    "X_test = mnist.test.images\n",
+    "Y_test = mnist.test.labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- A `next_batch` function that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Get the next 64 images array and labels\n",
+    "batch_X, batch_Y = mnist.train.next_batch(64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Link: http://yann.lecun.com/exdb/mnist/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "IPython (Python 2.7)",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}