Artificial Neural Networks are a fundamental part of Deep Learning. They are mathematical models of biological neural networks based on the concept of artificial neurons.

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 ...

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 ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases. Written by eWEEK content and product ...

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.

Sponsored Post Researchers at Google and Intel recently collaborated to extract the maximum performance from Intel® Xeon and Intel® Xeon Phi processors running TensorFlow*, a leading deep learning and ...

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.