# TensorFlow Tutorial - Convolutional Neural Network

Notes by Magnus Erik Hvass Pedersen: https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convol...

The previous tutorial showed that a simple linear model had about 91% classification accuracy for recognizing hand-written digits in the MNIST data-set.

In this tutorial we will implement a simple Convolutional Neural Network in TensorFlow which has a classification accuracy of about 99%, or more if you make some of the suggested exercises.

Convolutional Networks work by moving small filters across the input image. This means the filters are re-used for recognizing patterns throughout the entire input image. This makes the Convolutional Networks much more powerful than Fully-Connected networks with the same number of variables. This in turn makes the Convolutional Networks faster to train.

You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor. Beginners to TensorFlow may also want to study the first tutorial before proceeding to this one.