Deep Learning with PyTorch : Generative Adversarial Network

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Create Discriminator and Generator Network

Create a training loop to train GAN model

Clock2 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images. In order to be successful in this project, you will need to have a theoretical understanding on convolutional neural network and optimization algorithm like Adam or gradient descent. This project will focus more on the practical aspect of DCGAN and less on theoretical aspect. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

개발할 기술

  • Convolutional Neural Network
  • Python Programming
  • pytorch
  • Genrative Adversarial Network

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Setup Google Runtime

  2. Configurations

  3. Load MNIST Handwritten Dataset

  4. Load Dataset into Batches

  5. Create Discriminator Network

  6. Create Generator Network

  7. Create Loss Function and Load Optimizers

  8. Training GAN

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

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