Deep Learning with PyTorch : Build an AutoEncoder

提供方
Coursera Project Network
在此指導項目中,您將:

Create Custom Dataset

Create AutoEncoder Network

Train AutoEncoder Network

Clock1 hour
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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常見問題

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