/explore
|
You are here 
|
www.paepper.com | ||
| | | | |
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. |
|
| | | | | ||
| | | | |
sebastianraschka.com
Previously, I shared an article using multi-GPU training strategies to speed up the finetuning of large language models. Several of these strategies include... |
|
| | | | | ||
| | | | |
saturncloud.io
By combining Dask and PyTorch you can easily speed up training a model across a cluster of GPUs. But how much of a benefit does that bring? This blog post finds out! |
|
| | | | | ||
| | | | |
michael-lewis.com
An introduction to vector search (aka semantic search), and Retrieval Augmented Generation (RAG). |
|
| | | |||
