- Ease of programming (development and deployment)
- Running speed
- Truly open (open source with good governance)

University of Michigan - Ann Arbor

Instead of manually building up deep network architectures, many python libraries are available online to help users to easily implement deep learning models.

- Trax (built on TensorFlow)
- Tensor2Tensor (built on TensorFlow)
- Pytorch
- Fast.ai ( built on top of PyTorch)
- Keras (built to be a higher level framework to interface with other frameworks like TensorFlow)
- Vowpal Wabbit 
- Theano
- Caffee 

Deep Learning Python libraries (frameworks)

Natural Language Processing with Sequence Models is the third course in the Natural Language Prcessing Specialization published by deeplearning.ai.

Coursera: Natural Language Processing with Sequence Models

Criteria for choosing a deep learning framework

An open source deep learning library that specializes in tensor computations and GPU acceleration. It allows you to build neural networks and has plenty of features such as implementing custom layers and network architectures.

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