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Deep learning with dynamic computation graphs in TensorFlow
The main features of tensorflow/fold are: Deep Learning, Deep Learning Ecosystems, Deep Learning Frameworks, Machine Learning Frameworks.
Projects with overlapping indexed features include: google/qkeras — QKeras: a quantization deep learning library for Tensorflow Keras. keras-team/keras-contrib — Keras community contributions. catalyst-team/catalyst — Accelerated deep learning R&D. deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep… bsautermeister/tensorlight — TensorLight - A high-level framework for TensorFlow. maxpumperla/elephas — Distributed Deep learning with Keras & Spark.
Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation g
TensorLight - A high-level framework for TensorFlow
