Deep Learning with PyTorch : Build an AutoEncoder

提供方
Coursera Project Network
在此指导项目中,您将:

Create Custom Dataset

Create AutoEncoder Network

Train AutoEncoder Network

Clock1 hour
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

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

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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