# Handwritten Digit Recognition using TensorFlow with Python-2

### In this tensorlfow project, our goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.

#### Videos

Each project comes with 2-5 hours of micro-videos explaining the solution.

## What will you learn

• Understanding the problem statement

• Understanding complete Flowchart of how a Neural Network works

• Understanding One-hot encoded vectors

• Converting encoded vector to images using helper function

• What is Tensorflow and how does it works

• What are Placeholder variables

• Understanding a deep learning model and terms associated with it

• Softmax activation function

• "Gradient Descent Optimizer " and "Cross_entropy" loss function

• Defining and Initiating a Tensorflow session

• Plotting graphs after each epoch

• Ensembling different Neural Networks to create a sequence instead of the best Neural Network

• Plotting graphs for weights after each optimization iteration

• Making function for plotting graphs for each Convolution layer

• Saving and Restoring variables of a Neural Networks

• Visualization of Weights and Optimization iteration using Confusion Matrix

• Using Seaborn's heatmap function for visualizing Confusion Matrix

## Project Description

Start here if...

you’re new to computer vision. This project is the perfect introduction to techniques like neural networks using a classic dataset including pre-extracted features.

Project Description:

MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

In this TensorFlow project, our goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.

Practice Skills:

Computer vision fundamentals including simple neural networks

Data Introduction:

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.

The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.

Each pixel column in the training set has a name like pixelx, where x is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed $x$ as $x = i * 28 + j$, where $i$  and $j$  are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero).

Acknowledgements:

More details about the dataset, including algorithms that have been tried on it and their levels of success, can be found at http://yann.lecun.com/exdb/mnist/index.html. The dataset is made available under a Creative Commons Attribution-Share Alike 3.0 license.

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7-July-2017
02h 17m
8-July-2017
01h 31m