Deep learning networks mostly use neural network architectures and are hence often referred to as deep neural networks. The word “deep” refers to the number of hidden layers in the neural network.

Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first ...

Neural architecture search promises to speed up the process of finding neural network architectures that will yield good models for a given dataset. Topics Spotlight: New Thinking about Cloud ...

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

With the use of proper neural network architecture (number of layers, number of neurons, non-linear function, etc.) along with large enough data, a deep learning network can learn any mapping from ...

In this post, I will briefly review the deep learning architectures that help computers detect objects. Convolutional neural networks. One of the key components of most deep learning–based ...

Artificial Neural Network Architecture. Scientists design ANNs to function like neurons. 6 They write lines of code in an algorithm such that there are nodes that each contain a mathematical function, ...

This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.

Patch-based inference with MCUNetV2. To address the memory bottleneck of convolutional neural networks, the researchers created called MCUNetV2, a deep learning architecture that can adjust its ...