Deep Learning Frameworks
- 01. TensorFlow has a ecosystem of tools, libraries and community resources to push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- 02. Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
- 03. Caffe is a deep learning framework made with expression, speed, and modularity in mind
- 04. PyTorch - An open source machine learning framework that accelerates the path from research prototyping to production deployment.
- 05. Chainer - A Powerful, Flexible, and Intuitive Framework for Neural Networks
- 06. Apache MXNet - A truly open source deep learning framework suited for flexible research prototyping and production.
- 07. MATLAB for Deep Learning - Data preparation, design, simulation, and deployment for deep neural networks
- 08. Paddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice
- 09. Eclipse Deeplearning4j is 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 top of TensorFlow 2, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod
- 11. TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters.
- 12. DeepLearningKit – Open Source Deep Learning Framework for Apple iOS, OS X