Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to ...

Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that ...

Researchers at Soongsil University (Korea) published “A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration.” Abstract: “Over the past decade, deep-learning-based ...

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 ...

DL4J has a rich set of deep network architecture support: RBM, DBN, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), RNTN, and Long Short-Term Memory (LTSM) network.

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of ...

Convolutional neural networks (CNNs) are a class of deep neural networks commonly used in computer vision tasks such as image and video recognition, object detection and image segmentation.

The Quantum Convolutional Neural Network (QCNN) architecture is an innovative computational model that cleverly combines the parallelism of quantum computing with the feature extraction ...