Deep Learning with PyTorch : Build an AutoEncoder
12 ratings

Create Custom Dataset
Create AutoEncoder Network
Train AutoEncoder Network
12 ratings
Create Custom Dataset
Create AutoEncoder Network
Train AutoEncoder Network
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.
Deep Learning
Convolutional Neural Network
Autoencoder
Python Programming
pytorch
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Explore MNIST Handwritten digit dataset
Data Preparation
Load Dataset into batches
Create AutoEncoder Model
Train AutoEncoder Model
Plot Results
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
by YZ
Mar 21, 2021Great Short course. It would be great if I can download my notebook. or revisit the videos after completion.
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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