The Metaverse is a virtual world that is constantly expanding and evolving. As more and more people enter the Metaverse, the need for realistic artificial intelligence (AI) that can interact with users becomes increasingly important.
Bitsletter #4: In this episode we discuss how to debug models with overfitting, awaiting mulitple promises concurrently, the reasoning potential hidden in large language models and Remotion and tool to create videos with React.
Bitsletter #3: In this episode we discuss how to avoid hot-spots for ML predictions APIs, incremental static regeneration (ISR), Google Imagen and Hugging Face valuation.
Bitsletter #2: In this episode we discuss data centric modeling, lazy loading for faster web apps, self-supervised learning with Data2vec and DeepMind GATO generalist agent.
Bitsletter #1: in this first episode we talk about repeatable data splitting with hash functions, building web apps on the edge, probabilistic diffusion models and a new PyTorch release.
TorchData is a new library for better data loading in PyTorch. In this post I will show you how to use it and why it's better than Dataset and DataLoader.
Before moving to Spark, here is 3 tips to scale Pandas to bigger datasets.
Easily squeeze performance out of your scripts, with only slight modifications. Learn how to leverage caching easily in Python.
Python is great but lacks structure. I detail the issues and provide a GitHub template repository to solve the issue.
