A linear programming approach for optimizing features in ML models

Whether it’s iterating on Facebook’s News Feed ranking algorithm or delivering the most relevant ads to users, we are constantly exploring new features to help improve our machine learning (ML) models. Every time we add new features, we create a challenging data engineering problem that requires us to think strategically about the choices we make. More complex features and sophisticated techniques require additional storage space. Even at a company the size of Facebook, capacity isn’t infinite. If left unchecked, accepting all features would quickly overwhelm our capacity and slow down our iteration speed, decreasing the efficiency of running the models.

K-means for beginners: How to build from scratch in Python

In this article, you will learning how to implement k-means entirely from scratch and gain a strong understanding of the k-means algorithm.

Introducing Triton: Open-source GPU programming for neural networks

We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.

Image processing: Cartoonify your image using OpenCV-Python

Nowadays playing with images is a hobby of many people. People usually click pictures and add filters or customize the images with different stuff and put it on social media. Cartooning the image is a new trend. People use different apps to transform their images into cartoon images.

Using diffusion model to generate high fidelity images

Using super-resolution diffusion models, Google’s latest super-resolution research can generate realistic high-resolution images from low-resolution images, making it difficult for humans to distinguish between composite images and photos. Google uses the diffusion model to increase the resolution of photos, making it difficult for humans to differentiate between synthetic and real photos.