AI Workshop: Hands-on with GANs Using Dense Neural Networks

سرفصل های دوره

If you’re looking for hands-on AI practice, this workshop-style coding course was designed for you. Join instructor Janani Ravi as she shows you how to build and train generative adversarial networks (GANs). Explore the core components of GANs including how to set up the virtual environment, run the notebook server, instantiate the PyTorch Dataset, DataLoader, and more. Janani covers the basics of standalone training of adversaries, training GANs, and visualizing results. By the end of this course, you’ll also know how to address common problems associated with GANs and mitigate them effectively throughout the training process.

This course was created by Loonycorn. We are pleased to host this content in our library.

01 - Introduction

01 - Understanding generative modeling

02 - 1. Introducing Generative Adversarial Networks

01 - Course outline and prerequisites

02 - Set up the virtual environment and run the notebook server

03 - Introducing generative adversarial networks (GANs)

04 - Instantiating the dataset and data loader

05 - Viewing training data

03 - 2. Stand-Alone Training of Adversaries

01 - Big picture overview of a GAN

02 - Training the adversaries

03 - The generator architecture and discriminator architecture

04 - Understanding the generator and discriminator outputs

05 - Stand-alone training of discriminator as classification model

06 - Stand-alone training of generator

04 - 3. Training Generative Adversarial Networks

01 - Computing losses for generator and discriminator

02 - Understanding the minimax loss function

03 - Setting up GAN training

04 - Visualizing GAN training results

05 - Problems with GANs and potential mitigations

05 - Conclusion

01 - Summary and next steps

45,900 تومان

خرید اشتراک افزودن به سبد خرید

خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

تولید کننده: LinkedIn Learning (Lynda)