TensorFlow is the default back-end for Keras, and the one recommended for many use cases involving GPU acceleration on Nvidia hardware via CUDA and cuDNN, as well as for Tensor Processing Unit ...

Which of these deep learning frameworks should you use? In this article, we’ll take a high-level comparative look at TensorFlow, PyTorch, and JAX.

Developers can use TensorFlow Lite to easily add complex machine learning capabilities to their apps. Here is how.

While TensorFlow Lite enables you to run complex models on a Raspberry Pi, there are inherent limitations due to the device’s hardware constraints.

TensorFlow has become the most popular tool and framework for machine learning in a short span of time. It enjoys tremendous popularity among ML engineers and developers.

TensorFlow Lite is an open-source deep learning framework for on-device inference. The new tool is designed to adapt machine learning models to datasets with transfer learning.

While PyTorch is an excellent deep learning framework, there are other options worth exploring. TensorFlow, developed by Google, is a strong alternative, particularly for large-scale AI ...

In the release of TensorFlow 2.9, the performance improvements delivered by the Intel oneAPI Deep Neural Network Library are turned on by default.

Watch this video on YouTube. For more details on the new TensorFlow Lite 1.0 open source deep learning framework jump over to the official website by following the link below.

Despite some of the inherent complexities of using FPGAs for implementing deep neural networks, there is a strong efficiency case for using reprogrammable devices for both training and inference.

Wave Computing accelerates deep learning by using dataflow technology to eliminate the need for a host and co-processor in the processing of a neural network.