Jeremy Howard
NB: Please go to http://course.fast.ai/part2.html to view this video since there is important updated information there. If you have questions, use the forums at http://forums.fast.ai.
We start today with a deep dive into the DarkNet architecture used in YOLOv3, and use it to better understand all the details and choices that you can make when implementing a resnet-ish architecture. The basic approach discussed here is what we used to win the DAWNBench competition!
Then we’ll learn about Generative Adversarial Networks (GANs). This is, at its heart, a different kind of loss function. GANs have a generator and a discriminator that battle it out, and in the process combine to create a generative model that can create highly realistic outputs. We’ll be looking at the Wasserstein GAN variant, since it’s easier to train and more resilient to a range of hyperparameters.
Source
48:52 gans start
I get an error message: ModuleNotFoundError: No module named 'fastai'. What library do I have to install or import??
what does the "ni" mean in the self.conv1=conv_layer function mean??
I need information for my GAN to come up with a medicine therapeutic. Where can I go to get sample data for training??
Amazing lecture to learn GANs
can you publish the code yo used to demonstrate the GANs
Those medical texts at the start are rubbish ??