www.nature.com/scientificreports OPEN Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis Zhongzheng Li , Shenghui Wang , Huabin Zhao , Peishuo Yan , Hongmei Yuan , Mengxia Zhao , Ruyan Wan , Guoying Yu *...
On the other hand, an artificial neural network is used to construct the prediction model of influencing factors for green development behavior adopted by construction enterprises, which is a new application of this method into the field of the construction industry. In terms of practical ...
To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data ...
To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as terabytes or petabytes of data text or images or video from the internet. The training yields aneural networkof billions ofparametersencoded representation...
To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as terabytes or petabytes of data text or images or video from the internet. The training yields aneural networkof billions ofparametersencoded representation...
Fig. 2. (a) Processing neural network model (b) configuration of the ANN for the ENR-BP. The FF-BP-NN is crucial for synchronizing the processed data. Initially, input data introduced to the IL and forwarded within the ANN model along with the generated CWij to the HL, and the sum ...
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AI models can be very large, especially Deep Neural Networks(DNNs), and require massive computing power. Training these AI models requires highly parallelized tasks because the computations are independent of each other. This makes it a good use case for distributed processing on GPUs. With the ...
A more complete explanation of neural works is here.Deep is a technical term. It refers to the number of layers in a neural network. A shallow network has one so-called hidden layer, and a deep network has more than one. Multiple hidden layers allow deep neural networks to learn features...
In more intuitive terms, neurons can be understood as the subunits of a neural network in a biological brain. Here, the signals of variable magnitudes arrive at the dendrites. Those input signals are then accumulated in the cell body of the neuron, and if the accumulated signal exceeds a ...