Most deep learning books are based on one of several popular Python libraries such as TensorFlow, PyTorch, or Keras. In contrast ... the most basic element of deep learning.

Keras proper, a high-level front end for building neural network models, ships with support for three back-end deep learning ... the TensorFlow back end configured for CPU-only to do my basic ...

As you might guess from the name, PyTorch uses Python as its ... neural network models. Keras ships with support for three back-end deep learning frameworks: TensorFlow, CNTK, and Theano.

Notice you must import Keras, but you don't import TensorFlow explicitly. Many programmers who are new to Python are surprised to learn that base Python does not support arrays. NumPy arrays are used ...

The lethal combination of TensorFlow and Keras delivers the power and simplicity for building sophisticated deep learning models ... Its integration with Python IDEs such as PyCharm made ...

Initial integration with the Keras deep learning library began with the release of TensorFlow 1.0 in February 2017. It also promises three times faster training performance when using mixed ...

Google might just fix that. It's releasing an open source tool, Tensor2Tensor, that can quickly train deep learning systems using TensorFlow. In the case of its best training model, you can ...