Generative Models
Build GANs, VAEs, and understand diffusion model architecture.
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After this skill you can…
- Train a GAN on image data
- Explain the ELBO in VAEs
- Describe the forward and reverse diffusion process
Prerequisites
Watch (10 videos)
Deep Learning with PyTorch : Generative Adversarial Network
→ Implement a GAN to generate images→ Train a generator to produce realistic images
Karan Dalal - One Minute Video Generation with Test Time Training
→ Generate long-form videos using Test Time Training→ Apply Transformers to video generation tasks
L17.7 VAE Latent Space Arithmetic in PyTorch -- Making People Smile (Code Example)
→ Implement VAE latent space arithmetic in PyTorch→ Build generative models with PyTorch
Build a Generative Adversarial Neural Network with Tensorflow and Python | Deep Learning Projects
→ Build a GAN with Tensorflow→ Generate synthetic datasets with Python
DCGAN implementation from scratch
→ Implement a GAN from scratch→ Train a GAN on a dataset
Generative Deep Learning with TensorFlow
→ Build simple AutoEncoders with TensorFlow→ Implement neural style transfer using transfer learning
Pytorch Conditional GAN Tutorial
→ Train a Conditional GAN on a dataset→ Implement GAN architecture using PyTorch
Build Better Generative Adversarial Networks (GANs)
→ Build and evaluate GANs using FID→ Implement StyleGANs for generative modeling
GNN Project #4.1 - Graph Variational Autoencoders
→ Implement GVAE with PyTorch→ Train graph autoencoders
Neural Style Transfer Tutorial with Tensorflow and Python in 10 Minutes
→ Apply artistic styles to images using neural networks→ Build a neural style transfer model with Tensorflow
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