1. TensorFlow An ecosystem of tools, libraries, and community resources that lets developers easily build and deploy ML-powered applications at scale.
2. Aesara A Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays, built for deep learning research.
3. Caffe A deep learning framework made with expression, speed, and modularity in mind — developed at Berkeley Vision and Learning Center.
4. PyTorch An open-source machine learning framework that accelerates the path from research prototyping to production deployment, backed by Meta AI.
5. Chainer A powerful, flexible, and intuitive framework for neural networks — pioneering the define-by-run approach for dynamic computation graphs.
6. Apache MXNet A truly open-source deep learning framework suited for flexible research prototyping and scalable production deployments across multiple GPUs.
7. MATLAB for Deep Learning Complete tooling for data preparation, network design, simulation, and deployment of deep neural networks — integrated within the MATLAB environment.
8. PaddlePaddle PArallel Distributed Deep LEarning — an industrial-practice machine learning framework from Baidu supporting large-scale distributed training.
9. Eclipse Deeplearning4j The first commercial-grade, open-source, distributed deep-learning library written for Java and Scala — integrated with Hadoop and Apache Spark.
10. Keras Exascale machine learning built on TensorFlow 2 — an industry-strength framework that scales to large clusters of GPUs or an entire TPU pod.
11. TensorFlowOnSpark Brings scalable deep learning to Apache Hadoop and Apache Spark clusters — enabling TensorFlow model training within existing big data pipelines.
12. DeepLearningKit An open-source deep learning framework designed specifically for Apple iOS and OS X — enabling on-device neural network inference on Apple hardware.