Both PyTorch and TensorFlow support deep learning and transfer learning. Transfer learning, which is sometimes called custom machine learning, starts with a pre-trained neural network model and ...

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. In this article, the author explains how to use Tensorflow.NET to build a neural network.

A TensorFlow-specific implementation of Keras (a high-level neural networks API that in its standard implementation also runs on top of MXNet, Deeplearning4j, Microsoft Cognitive Toolkit, and ...

Discover the best deep learning software for training and deploying neural networks with powerful features and customizable options. Written by eWEEK content and product recommendations are ...

By taking advantage of similarities in the data values that are input into a neural network layer, DR eliminates redundant computation during inference, reducing the total time taken.

Other optimizations to TensorFlow components resulted in significant CPU performance gains for various deep learning models. Using the Intel MKL imalloc routine, both TensorFlow and the Intel MKL-DNN ...

The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on.

At its core, deep learning is a subfield of artificial intelligence that focuses on building and training neural networks capable of performing complex tasks through pattern recognition and data ...