Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows. 

Kur - Descriptive Deep Learning

Sonnet is a library built on top of TensorFlow for building complex neural networks. The library uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation. Modules are called with some input Tensors, which adds ops to the Graph and returns output Tensors. 
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Sonnet is designed specifically to work with TensorFlow, and as such does not prevent you from accessing the underlying details such as Tensors and variable_scopes. Models written in Sonnet can be freely mixed with raw TF code, and that in other high level libraries.

Sonnet is a library built on top of TensorFlow for building complex neural networks. The library uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation. Modules are called with some input Tensors, which adds ops to the Graph and returns output Tensors. 

Sonnet - Library built on top of TensorFlow for building complex neural networks

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Kur - Descriptive Deep Learning

Sonnet - Library built on top of TensorFlow for building complex neural networks

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Kur - Descriptive Deep Learning

Sonnet - Library built on top of TensorFlow for building complex neural networks

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