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Last updated : Nov 30, 2017
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
Nov 2017
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Keras is a high-level interface in Python for building neural networks. Created by a Google engineer, Keras is open source and runs on top of either TensorFlow or Theano. It provides an amazingly simple interface for creating powerful deep-learning algorithms to train on CPUs or GPUs. Keras is well designed with modularity, simplicity, and extensibility in mind. Unlike a library such as Caffe, Keras supports more general network architectures such as recurrent nets, making it overall more useful for text analysis, NLP and general machine learning. If computer vision, or any other specialized branch of machine learning, is your primary concern, Caffe may be a more appropriate choice. However, if you’re looking to learn a simple yet powerful framework, Keras should be your first choice.

Mar 2017
Assess ?

Keras is a high-level interface in Python for building neural networks. Created by a Google engineer, Keras is open source and runs on top of either TensorFlow or Theano. It provides an amazingly simple interface for creating powerful deep-learning algorithms to train on CPUs or GPUs. Keras is well designed with modularity, simplicity, and extensibility in mind. Unlike a library such as Caffe, Keras supports more general network architectures such as recurrent nets, making it overall more useful for text analysis, NLP and general machine learning. If computer vision, or any other specialized branch of machine learning, is your primary concern, Caffe may be a more appropriate choice. However, if you're looking to learn a simple yet powerful framework, Keras should be your first choice.

Veröffentlicht : Mar 29, 2017

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