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- Level Beginner
- Duration 3 hours
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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.Auto Summary
Discover how to implement an autoencoder using PyTorch in this one-hour, project-based course. Ideal for those in IT and Computer Science, you'll learn how to encode and decode images to reconstruct them, specifically focusing on denoising handwritten digits. The course is perfect for learners familiar with Python and basic PyTorch concepts, including neural networks and convolutional neural networks. Offered by Coursera, this foundational course is available through a Starter subscription. Join now to enhance your deep learning skills with PyTorch!