1. TensorFlow:
Pros: TensorFlow supports a large amount of infrastructure, from mobile and web-based applications to distributed clusters and AI accelerators.
Cons: TensorFlow’s graph-based approach may require more time and knowledge to understand and build complex models.
2. Keras:
Pros: Keras offers high-level API flexibility with openness to choose backend to simplify the creation of complex models.
Cons: Limited support for recurrent neural networks, and requires more effort to customize layers and build custom architectures.
3. MXNet:
Pros: MXNet provides support for many languages, including JavaScript, with flexibility to scale quickly on cloud- and cluster-based architectures.
Cons: Limited deficiency in Documentation compared to other libraries, may require more coding knowledge to understand and use.
4. Caffe2:
Pros: Caffe2 supports cutting edge research with optimized models and procedures.
Cons: Caffe2 Move API may require additional programming to integrate with existing models and applications.