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PROJECT
Build a Classifier for Handwritten Digits
In this unguided project, you’ll use a deep neural network to recognize handwritten digits. You'll use the images of handwritten digits from the MNIST dataset.
You will learn to:
Load the MNIST dataset using PyTorch.
Build a deep neural network using PyTorch.
Train a deep neural network using PyTorch.
Convert image data to tensors.
Skills
Machine Learning
Neural Networks
Deep Neural Networks
Prerequisites
Intermediate knowledge of Python
Basic knowledge of Matplotlib
Basic knowledge of neural networks
Technologies
Python
PyTorch
Project Description
It might be easy for us to recognize the blurred image given below because of the brain’s amazing functionality, but it’s not as simple for computers.
We can recognize it with such ease, possibly because we’ve seen so many different variants of the digit “6” that our brain has learned to recognize it in various forms. There is an area of machine learning called deep learning that mimics this learning mechanism of the human brain—learning by example.
Deep learning is based on neural networks that are made up of different layers of linked “neurons.” It is inspired by the structure and function of the neurons in the human brain. We need massive amounts of data and complex algorithms to train a neural network.
In this unguided project, we will use PyTorch to implement a deep learning algorithm to recognize handwritten digits. We will use the MNIST dataset to complete this project. It is a collection of 70,000 handwritten digits divided into a training set of 60,000 images and a test set of 10,000 images.
Project Tasks
Task 1: Import Modules
Task 2: Download and Load the Datasets
Task 3: Build and Train the Model
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.