Papers With Code currently hosts the implementation of more than 40,000 machine learning research papers. “PapersWithCode plays an important role in highlighting papers that are reproducible.

Read Lones’s full paper, titled, “How to avoid machine learning pitfalls: a guide for academic researchers,” for more details about common mistakes in the ML research and development process.

This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly ...

Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks.New libraries like JAX, ...

This project's goal was the classification of road types across all of Michigan. Machine learning models were used to classify multi-spectral, multi-resolution imagery of Michigan land to pick out and ...

Apple's machine learning researchers have worked on myriad ways to improve Apple Intelligence and other generative AI systems, as its research papers accepted by a major AI conference demonstrate.

According to a number of research initiatives (e.g., Hidden Debt in Machine Learning Systems) technical debt resides in areas common to many machine learning projects: Data Quality, Model Quality ...

The second key to a successful machine learning (ML) project is an ability to process collected data. The introduction of general-purpose GPUs in 2006 and their continued evolution has unlocked ...