Sebastian Raschka's Blog, page 5

Conversational chat bots such as ChatGPT probably will not be able replace traditional search engines and expert knowledge anytime soon. With the vast amount of misinformation available on the internet, the ability to distinguish between credible and unreliable sources remains challenging and crucial.

Imagine you want to quickly train a few machine learning or deep learning models on the cloud but don't want to deal with cloud infrastructure. This short article explains how we can get our code up and running in seconds using the open source lightning library.

Recently, I shared the top 10 papers that I read in 2022. As a follow-up, I am compiling a list of my favorite 10 open-source releases that I discovered, used, or contributed to in 2022.

Every day brings something new and exciting to the world of machine learning and AI, from the latest developments and breakthroughs in the field to emerging trends and challenges. To mark the start of the new year, below is a short review of the top ten papers I've read in 2022.

About monthly machine learning musings, and other things I am currently workin on ...

Occasionally, I share research papers proposing new deep learning approaches for tabular data on social media, which is typically an excellent discussion starter. Often, people ask for additional methods or counterexamples. So, with this short post, I aim to briefly summarize the major papers on deep tabular learning I am currently aware of. However, I want to emphasize that no matter how interesting or promising deep tabular methods look, I still recommend using a conventional machine learning method as a baseline. There is a reason why I cover conventional machine learning before deep learning in my books.

Regarding neural network training, I think we are all guilty of doing this: we choose our batch sizes as powers of 2, that is, 64, 128, 256, 512, 1024, and so forth. There are some valid theoretical justifications for this, but how does it pan out in practice? We had some discussions about that in the last couple of days, and here I want to write down some of the take-aways so I can reference them in the future. I hope you'll find this helpful as well!

In this article, we will take deploy a Super Resolution App on the cloud using lightning.ai. The primary goal here is to see how easy it is to create and share a research demo. However, the cloud is for more than just model sharing: we will also learn how we can tap into additional GPU resources for model training.

In this post, we will build a Lightning App. Why? Because it is 2022, and it is time to explore a more modern take on interacting with, presenting, and sharing our deep learning models. We are going to tackle this in three parts. In this first part, we will learn what a Lightning App is and how we build a Super Resolution GAN demo.

The PyTorch team recently announced TorchData, a prototype library focused on implementing composable and reusable data loading utilities for PyTorch. In particular, the TorchData library is centered around DataPipes, which are meant to be a DataLoader-compatible replacement for the existing Dataset class.