BMXNet is an open-source library for binary neural networks, released in 2017 by the Hasso Plattner Institute in Potsdam and built on the Apache MXNet framework. Binary networks store weights and activations with a single bit instead of 32, making models far smaller and faster – attractive for smartphones and embedded devices, though usually at some cost to accuracy. Since MXNet was retired in 2023, BMXNet is now mainly of research interest (as of October 2026).
Apache MXNet was an open-source deep learning framework that could train models efficiently across multiple GPUs and machines, with interfaces for Python, C++, R, Scala and other languages. Amazon Web Services backed it as its preferred framework for some years. The Apache Software Foundation retired the project in September 2023 and moved it to the Apache Attic by February 2024; it is no longer developed. Existing models are usually migrated to other frameworks.
Artificial neural networks (ANNs) are computational models made up of many connected units (“neurons”) arranged in layers that process inputs via weighted connections. During training, the weights are adjusted step by step using examples, usually by backpropagation. ANNs are the foundation of deep learning and power image recognition, speech recognition and large language models. Despite their biological inspiration, they work far more simply than the human brain.
Deep learning is a branch of machine learning that uses artificial neural networks with many layers. Such networks learn from large amounts of data which features matter – from edges in images to relationships of meaning in texts. Deep learning underpins today's language models, image and speech recognition and machine translation, and usually requires considerable computing power and training data.
