Explore >> Select a destination

You are here

 sebastianraschka.com
Losses Learned
| | magazine.sebastianraschka.com
Tips for LLM Pretraining and Evaluating Reward Models
0.7 parsecs away

Travel
| | I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.
| | dennybritz.com
Implementing a Neural Network from Scratch in Python ยท Denny's Blog
2.2 parsecs away

Travel
| | All the code is also available as an Jupyter notebook on Github.
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
1.9 parsecs away

Travel
| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | www.paepper.com
Rethinking Batch in BatchNorm :: Read. Hack. Learn. Repeat. - This blog features state of the art applications in machine learning with a lot of PyTorch samples and deep learning code. You will learn about neural network optimization and potential insights for artificial intelligence for example in the medical domain.
11.6 parsecs away

Travel
| Today's paper: Rethinking 'Batch' in BatchNorm by Wu & Johnson BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This is a citation from the paper's abstract and the emphasis is mine which caught my attention. Let's explore these subtle ways which can negatively impact your model's performance! The paper of Wu & Johnson can be found on arxiv.