There are many different types of neural networks. The operation of the single unit is almost universal and the units are usually but not always, arranged in distinct layers with weighted connections between the layers. Using a neural network to obtain reasonably good results is not difficult. ...
Machine learning (ML), a subset of AI, involves systems that can "learn" from data. These algorithms improve their performance as the number of datasets they learn from increases. Deep learning, a further subset of machine learning, uses artificial neural networks to make decisions and prediction...
However, why neural networks give superior classification is not clearly explained in the literature. Particularly, the relationship between neural networks and traditional classification theory is not fully recognized [51]. In this paper, we provide explanation that neural network outputs are estimates ...
Moravec’s paradoxexplained that computers can easily prove theorems and solve mathematical problems that are easy for computers, but struggle with recognizing a face or moving around safely. It’s why vision and robotics sensorimotor research struggled so much during the 1970s and 1980s. ...
However, the common way to avoid the trap of going to a local minimum is modifying weights after each processed input of the training set. When all inputs from a training set are processed, oneepochis done. It is necessary to do multiple epochs to get the best results. The explained pro...
For this reason, it is essential to implement some of the Big Data techniques explained in Section 2 to be capable of processing and analyzing all these amounts of data generated. In addition, the electrical grid topology is an essential input for other energy services like observability, non-...
(QSAR) model32, was trained on low soot scale data. TheMedAEevaluated on 59 components is similar to the proposed model’s resultingMedAEon 43 test set components from the low soot scale. Table3reportsMedAEon mixtures, slightly higher thanMedAEfor single components, explained by the scarcity ...
Generative adversarial networks (GANs) explained Learn about the different aspects and intricacies of GANs, a type of neural network with applications both in and outside of the AI space. Unsupervised learning for data classification Discover the theory and concepts of unsupervised learning, a techniqu...
Artificial Intelligence, Machine Learning & Deep Learning explained: AI vs ML vs DL discussed. Read about types of Machine Learning - Explanation & Dependencies.
In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials. CANNs are able to incorporate by their very design information from three different sources, namely stress-stra...