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Atualizado em : May 19, 2020
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
May 2020
Experimente ? Vale a pena ir atrás. É importante entender como desenvolver essa capacidade. As empresas devem experimentar esta tecnologia em um projeto que possa lidar com o risco.

Nossos times continuam a usar e apreciar o framework de aprendizado de máquina PyTorch, e vários times preferem PyTorch a TensorFlow. O PyTorch expõe o funcionamento interno de ML que o TensorFlow oculta, facilitando a depuração, além de conter construções com as quais as pessoas programadoras estão familiarizadas, como ciclos e ações. As versões recentes melhoraram o desempenho do PyTorch, e o usamos com sucesso em projetos de produção.

May 2018
Avalie ? Vale a pena explorar com o objetivo de compreender como isso afetará sua empresa.

PyTorch is a complete rewrite of the Torch machine learning framework from Lua to Python. Although quite new and immature compared to Tensorflow, programmers find PyTorch much easier to work with. Because of its object-orientation and native Python implementation, models can be expressed more clearly and succinctly and debugged during execution. Although many of these frameworks have emerged recently, PyTorch has the backing of Facebook and broad range of partner organisations, including NVIDIA, which should ensure continuing support for CUDA architectures. ThoughtWorks teams find PyTorch useful for experimenting and developing models but still rely on TensorFlow’s performance for production-scale training and classification.

Nov 2017
Avalie ? Vale a pena explorar com o objetivo de compreender como isso afetará sua empresa.

PyTorch is a complete rewrite of the Torch machine learning framework from Lua to Python. Although quite new and immature compared to Tensorflow, programmers find PyTorch much easier to work with. Because of its object-orientation and native Python implementation, models can be expressed more clearly and succinctly and debugged during execution. Although many of these frameworks have emerged recently, PyTorch has the backing of Facebook and broad range of partner organisations, including NVIDIA, which should ensure continuing support for CUDA architectures. Thoughtworks teams find PyTorch useful for experimenting and developing models but still rely on TensorFlow’s performance for production-scale training and classification.

publicado : Nov 30, 2017
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