Deep Learning with PyTorch : Build an AutoEncoder

Offered By
Coursera Project Network
In this Guided Project, you will:

Create Custom Dataset

Create AutoEncoder Network

Train AutoEncoder Network

Clock1 hour
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In these one hour project-based course, you will learn to implement autoencoder using PyTorch. An autoencoder is a type of neural network that learns to copy its input to its output. In autoencoder, encoder encodes the image into compressed representation, and the decoder decodes the representation to reconstruct the image. We will use autoencoder for denoising hand written digits using a deep learning framework like pytorch. This guided project is for learners who want to use pytorch for building deep learning models.Learners who want to apply autoencoder practically using PyTorch. In order to be successful in this project, you should be familiar with python , basic pytorch like creating or defining neural network and convolutional neural network.

Skills you will develop

  • Deep Learning
  • Convolutional Neural Network
  • Autoencoder
  • Python Programming
  • pytorch

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Explore MNIST Handwritten digit dataset

  2. Data Preparation

  3. Load Dataset into batches

  4. Create AutoEncoder Model

  5. Train AutoEncoder Model

  6. Plot Results

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.