Deep Learning with PyTorch : Generative Adversarial Network

提供方
Coursera Project Network
在此免費的指導 項目中,您將:

Create Discriminator and Generator Network

Create a training loop to train GAN model

在面試中展現此實踐經驗

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

必備條件

Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)

您要培養的技能

Convolutional Neural NetworkPython ProgrammingpytorchGenrative Adversarial Network

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. 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

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

常見問題

常見問題

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