Explore >> Select a destination
|
You are here 
|
tiao.io | ||
| | | | |
blog.keras.io
|
|
| | | | | [AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics. | |
| | | | |
www.nicktasios.nl
|
|
| | | | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the second post, we will bu | |
| | | | |
vxlabs.com
|
|
| | | | | I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time. | |
| | | | |
web.navan.dev
|
|
| | | Tutorial on creating an image classifier model using TensorFlow which detects malaria | ||
