Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.

Distributed platform for rapid Deep learning application development. Written on Python, uses OpenCL or CUDA, employs Flow-Based Programming, under Apache 2.0.

MLlib is Apache Spark's scalable machine learning library, with APIs in Java, Scala, Python, and R. It features a large database of algorithms focusing on classification, regression, clustering and collaborative filtering.

Intel®'s reference deep learning framework committed to best performance on all hardware. Our focus was on a well-known hotspot (dilated convolution) in the SSD model. To accelerate convolution layer with dilation, the vanilla Python implementation was replaced with C kernels for matrix multiplication. The detection-output layer in inference path was also optimized around better use of these kernels.

Diffblue is a world leader in AI that understands code. Our goal is to automate all traditional coding tasks: bug fixing, test writing, finding and fixing exploits, refactoring code, translating from one language to another, and creating original code to fit specifications. As we progress we are commercialising our blue sky research into a suite of products. Our vision is to make our products ubiquitous, and to make code safer and better and cheaper to produce. We are always looking to hire new talent.

IBM iWatson is a platform that houses an array of tools designed for both developers and business users. Open APIs are available for users to access sample code, starter kits and can build cognitive search engines and virtual agents.