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 ...

Keras is one of the leading high-level neural networks APIs. It is written in Python and supports multiple back-end neural network computation engines. Given that the TensorFlow project has ...

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 ...

Why choose Python ... TensorFlow excels are: Keras is a popular open-source neural network library for the development and evaluation of neural networks within machine learning and deep learning ...

Key Takeaways Books help explain ML in depth, better than short tutorials.The right book depends on goals—coding, theory, or ...

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 ...

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 ...

In this video from the 2019 OpenFabrics Workshop in Austin, Xiaoyi Lu from Ohio State University presents: Accelerating TensorFlow with RDMA for High-Performance Deep Learning. Google’s TensorFlow is ...