Model Training

MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios. MindSpore is designed to provide development experience with friendly design and efficient execution for the data scientists and algorithmic engineers, native support for Ascend AI processor, and software hardware co-optimization. At the meantime MindSpore as a global AI open source community, aims to further advance the development and enrichment of the AI software/hardware application ecosystem.

JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research.

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research

ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML

Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app

Alink is a general algorithm platform based on Flink, developed by the Alibaba computing platform PAI team. Welcome everyone to join the Alink open source user Dingding group for communication.

FATE is an open-source project initiated by Webank’s AI Department to provide a secure computing framework to support the federated AI ecosystem.

The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the details.

Gensim is an open-source library for unsupervised topic modeling and natural language processing, using modern statistical machine learning. Gensim is implemented in Python and Cython for performance.

MLlib is Apache Spark’s scalable machine learning library.

Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures

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.

Fastai is an open-source deep learning library that sits on top of PyTorch. It provides high-level components for building neural networks with PyTorch. Generally speaking, Fastai can be thought of as a high-level API for PyTorch. Fastai also provides free online courses for studying deep learning.

Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia

ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks.

LightGBM is a distributed gradient boosting framework that uses tree-based learning. It is histogram-based and places continuous values into discrete bins leading to faster training and more efficient memory usage.

PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework.

PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab

XGBoost is an open-source gradient boosting framework library for C++, Java, Python, R, Julia, Perl, and Scala. The library works on Linux, Windows, and macOS. Developers love it for its accuracy, efficiency, and feasibility.

atBoost is a depth-wise gradient boosting library developed by Yandex. It uses oblivious decision trees to grow a balanced tree. It also uses the same features to make left and right splits for each level of the tree.

Catalyst is PyTorch framework for Deep Learning research and development. You get a training loop with metrics, model checkpointing, advanced logging and distributed training support without the boilerplate.

TensorFlow is an open-source machine learning library that supports multiple programming languages including Python and Javascript. TensorFlow provides various tools for building and bringing deep learning models to production. The library became more popular in its second version(TensorFlow 2.x) because of integrating Keras as its high-level API. Keras is an open-source library that provides a simple to use Python interface for neural networks.

Scikit-learn is a free and open-source machine learning library for the Python programming language. It offers tools for solving clustering, classification, regression, and unsupervised problems.