Previously, S&P only had data on about 2 million SMEs, but its AI-powered RiskGauge platform expanded that to 10 million.

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning!

See how DenseNets reuse features across layers, reduce parameters, and improve training efficiency.

My two favorite AI apps on Linux - and how I use them to get more done I was totally against AI until I found two tools that make handling all the research I do every day much easier.

WASHINGTON – March 11, 2025 – A new study published by the American Institute of Architects (AIA) quantifies current adoption and use of artificial intelligence (AI) across the profession, as well as ...

Recently, many deep models have been proposed in different fields, such as image classification, object detection, and speech recognition. However, most of these architectures require a large amount ...

The demand for optimized inference workloads has never been more critical in deep learning. Meet Hidet, an open-source deep-learning compiler developed by a dedicated team at CentML Inc. This ...

10. MXNet Closing out our list of the 10 best Python libraries for deep learning is MXNet, which is a highly scalable open-source deep learning framework. MXNet was designed to train and deploy deep ...

In recent years, deep learning (DL) applications have been widely used in both industrial and academic domains. Bugs in the DL framework have become one of the leading causes of DL model training and ...