Examples of using Keras in English and their translations into Chinese
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Deep-learning libraries: TensorFlow and Keras.
TFLearn and Keras for higher level.
和Keras更高层次.First, what exactly is Keras?
首先，Keras到底是什么?TFLearn and Keras for higher level.
和Keras更高层次.Learn, TFLearn and Keras for higher level abstraction.
Learn，TFLearn和Keras的更高层次的抽象。Keras(2) is highest ranked non-framework library.
Keras（2）是排名最高的非结构库。Keras vs. PyTorch: Exporting models and cross-platform portability.
KerasvsPyTorch：导出模型和跨平台可移植性.Keras is the most popular front-end for deep learing.
Keras是深度学习上最受欢迎的前端库.And finally, let's look at the Keras.
最后，我们来看看Keras。Keras is the most popular front-end for deep learing.
Keras是最为流行的深度学习前端.Keras: Deep learning library for theano and tensorflow.
Keras：深的学习替西诺和TensorFlow图书馆。Keras is convenient because it can output the architecture easily.
Keras很方便，因为它可以轻松地输出架构。Keras is the most popular front-end for deep learing.
Keras是最流行的深度学习前端.And finally, let's look at the Keras.
最后，我们来看看Keras。Keras, a recommended high-level API for TensorFlow 2.0.
Keras，希望将它作为TensorFlow2.0中的推荐高级API。TensorFlow developers seem to be promoting Keras as tf.
而且TensorFlow开发人员似乎正在推广Keras，或者应该叫tf.Keras: Deep learning library for theano and tensorflow.
Keras：深度学习框架，集成了Theano和TensorFlow.François Chollet is the main creator of the Keras framework.
本书作者FrancoisChollet，是Keras框架的构建者。Keras vs. PyTorch: Popularity and access to learning resources.
KerasvsPyTorch：流行度和可获取学习资源.Keras vs. PyTorch: Popularity and access to learning resources.
Keras对比PyTorch：人气和可获取学习资源.
