Deep learning Products

The Mark I Perceptron was the first hardware implementation of the perceptron algorithm: a custom analog computer built at Cornell Aeronautical Laboratory for image recognition, with photocells as input and motor-driven potentiometers as learnable weights.

A two-volume MIT Press work by Rumelhart, McClelland and the PDP Research Group, published in 1986, that set out the parallel distributed processing framework — networks of simple units with hidden layers and sigmoid activation — and became a landmark of connectionism.

ALVINN was a 1988 Carnegie Mellon neural network that learned to steer the Navlab van by watching a human driver, an early demonstration of learned end-to-end driving.

CIFAR-10 is a labelled image dataset of 60000 32×32 colour images in 10 mutually exclusive classes, split into 50000 training and 10000 test images; it was created by Krizhevsky, Nair and Hinton for training and comparing image classifiers.

TensorFlow is Google's second-generation machine learning system, released as an open-source library under the Apache 2.0 license on 9 November 2015, succeeding the earlier DistBelief system.

PyTorch is an open-source deep-learning framework released by Meta in 2017, now governed by the PyTorch Foundation under the Linux Foundation. It is used both for research and for training and running large models in production.

JAX is a Python library for accelerator-oriented array computation and program transformation, aimed at high-performance numerical computing and large-scale machine learning. It pairs a NumPy-style API with composable transformations for compilation, batching, differentiation and parallelization.

NETtalk is an artificial neural network built by Terrence Sejnowski and Charles Rosenberg that learns to convert written English into phonemes and audible speech, showing that a network trained on examples can generalize to unseen words.

Torch is a scientific computing framework for machine learning built on the LuaJIT scripting language with a C/CUDA core, designed to put GPUs first and surrounded by a large ecosystem of community packages.

Theano is a Python library and optimizing compiler for mathematical expressions, especially matrix-valued ones, developed at the Montreal Institute for Learning Algorithms. Released in 2007, it combined symbolic differentiation, GPU execution and C code generation for numerically heavy research and teaching.

Caffe is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying convolutional neural networks, described in a 2014 paper by Yangqing Jia and colleagues.

Keras is a deep learning API written in Python that runs on top of JAX, TensorFlow or PyTorch. Since 2015 it has offered a high-level way to define, train and export neural networks without tying a project to one framework.

PaddlePaddle (飞桨) is Baidu's open-source deep learning framework, released in September 2016 out of an internal platform begun in 2013; at launch it was described as China's first open-source deep learning platform.

ONNX is an open format for representing machine learning models, announced by Microsoft and Facebook in September 2017 so that developers can move models between frameworks instead of converting them between incompatible formats.

Shenmai FFR is a software package that applies deep learning to CT coronary angiograms to estimate coronary flow reserve, helping clinicians judge whether a narrowing will cause myocardial ischaemia. It holds medical device approvals in China, the EU and the US.

Triton is OpenAI's open-source, Python-like language and compiler for writing GPU kernels. It lets researchers with no CUDA experience produce efficient GPU code, often close to what an expert would write.

MLX is an open-source machine learning framework from Apple's machine learning research team, released in December 2023 and built primarily for Apple silicon. Its NumPy-like Python API and unified-memory design let the CPU and GPU work on the same data.

Transformers is an open-source Python library that offers pretrained Transformer models through one unified API. Its paper was posted to arXiv on 9 October 2019, and the first v5 release candidate came in December 2025.

Apache MXNet is an open-source deep learning library that offers both symbolic and imperative programming, runs from mobile devices to GPU clusters, and ships bindings for eight languages. Its paper was posted in December 2015 by Tianqi Chen, Mu Li and colleagues.

MindSpore is Huawei's open-source AI computing framework, announced in March 2020, built to run across device, edge and cloud, co-optimized with Huawei's Ascend processors and shipped with tools for training visualization and model security.