MNIST Dataset: Variations in Handwritten Digits

Explore the MNIST dataset to recognize handwritten digits using neural networks.

Learn how machine learning models can accurately identify handwritten numbers using the widely-used MNIST dataset. Gain insights into the challenges and variations that neural networks overcome in digit recognition.

Key Insights

  • The MNIST dataset, commonly utilized in machine learning, includes a large range of handwritten digits that vary significantly in style, orientation, and stroke, demonstrating the complexity involved in digit recognition tasks.
  • Accurately recognizing digits such as the number "7"—which can appear in different formats ranging from simple strokes to elaborate variations—requires sophisticated models like neural networks due to their capability to handle such complexity and diversity.
  • The article briefly mentions how to display an image stored on Google Drive using the image library, highlighting practical skills related to handling and visualizing datasets for machine learning purposes.

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Let's talk about the data we're working with and the problem we're trying to solve. We're going to be working with the MNIST dataset. That's the Modified National Institute of Standards and Technology Database.

It's a very popular database of handwritten digits and it's commonly used for trying to train a machine learning tool to a machine learning model to recognize digits and it's also used for machine learning quite often. Let's take a look at displaying your own image stored on Google Drive. We've done this for you but it's really not very hard to do this, how to display an image using the image library.

But let's take a look at this image which we grabbed from Wikipedia to show you the kind of digits we're going to be working with. So these are the MNIST, this is a sample of the MNIST handwritten digits. And you can see how much variation there is among the zeros, handwritten, among the ones, look at all the crazy different directions they're leaning.

This one is almost 45 degrees. The quite variation among how people draw twos, a few more with loops than I would have thought. These threes are, every one of these numbers is particularly apparent how many different styles you can have.

So having a system that can learn how to recognize this as a seven with a little extra line down and the line through it versus this one which is like in a very bold stroke whereas this one which is who knows what's up with that seven to just a very simple seven like this one, a more standard seven. But you know there aren't very many standard sevens. So having a system that can recognize all of these and can identify with great accuracy each of these, that's a very tough challenge unless you're using a neural network.

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Let's dive into that data even more in the next lesson.

Colin Jaffe

Colin Jaffe is a programmer, writer, and teacher with a passion for creative code, customizable computing environments, and simple puns. He loves teaching code, from the fundamentals of algorithmic thinking to the business logic and user flow of application building—he particularly enjoys teaching JavaScript, Python, API design, and front-end frameworks.

Colin has taught code to a diverse group of students since learning to code himself, including young men of color at All-Star Code, elementary school kids at The Coding Space, and marginalized groups at Pursuit. He also works as an instructor for Noble Desktop, where he teaches classes in the Full-Stack Web Development Certificate and the Data Science & AI Certificate.

Colin lives in Brooklyn with his wife, two kids, and many intricate board games.

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