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DNN Platform (formerly "DotNetNuke Community Edition" content management system) is open source software distributed under an MIT License that is intended to allow management of websites without much technical knowledge, and to be extensible through a large number of third-party apps to provide functionality not included in the DNN core modules.
DNN Corp. was founded in 2006 by the leadership of the DotNetNuke open source project - Shaun Walker, [2] Nik Kalyani, Joe Brinkman and Scott Willhite. In November 2008 the company raised Series A round financing from Sierra Ventures and August Capital. The company is headquartered in San Mateo, California. In February 2009 the company launched ...
The task is to predict the efficacy of a given molecule for a specific medical application, like eliminating E. coli bacteria. The key design element of GNNs is the use of pairwise message passing , such that graph nodes iteratively update their representations by exchanging information with their neighbors.
This model paved the way for research to split into two approaches. One approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence. In the late 1940s, D. O. Hebb [14] proposed a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian ...
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output.With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.