MNIST Dataset : Digit Recognizer Data Science Project

In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.

Videos

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

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

What will you learn

  • Working on Computer Image Data using Tensor flow

  • Inception Model of Tensor flow

  • Image Data Recognition using Transfer Learning

  • Video Recognition of Neural Network

  • Learn Advanced Model of Neural Network using Tensor flow

Project Description

Start here if...

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

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 data science 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.

 

Similar Projects

Big Data Project Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.
Big Data Project Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
Big Data Project Deep Learning with Keras in R to Predict Customer Churn
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
Big Data Project 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.

Curriculum For This Mini Project

 
  4-Aug-2017
01h 26m
  5-Aug-2017
01h 34m