… Neural Network (CNN) can be implemented in APL. Its first-class array support ideally fits
that domain, and the operations of APL … as ten lines of native APL. All these blocks are purely …

… A neural network with just two layers (one input layer and one output layer) and no feedback
… in neural networks for several years. Current research usually uses neural networks with at …

… In this paper we have presented an APL tool for the early detection of company failures using
neural networks. These computing devices proved themselves to he a viable alternative to …

Artificial neural networks (ANNs) have … neural networks. We investigate the integration of
more biologically plausible mechanisms, such as synaptic plasticity, to improve these networks’ …

… of a deep neural network architecture concept based on the … in a photonic artificial neural
network. After the description of the … -optical neuron and therefore an all-optical neural network (…

… describes a curriculum unit in which APL is used to introduce models of neural networks. …
for simulating neural networks with computer programs. Then several neural net models are …

… For example, in such complex systems as nuclear power plants, neural networks … This is
another benefit of ALNs over neural networks, since we do not need to keep the whole network …

… APL against the current crop of specialized neural network frameworks in the form of PyTorch.
We compare performance as well as the language design of APL for neural network … ” APL …

… intelligence (AI) and deep neural networks (DNNs), is … neural networks (PNNs) implemented
on silicon integration platforms stand out as a promising candidate to endow neural network …

In this paper, we develop a deep learning approach for the accurate solution of challenging
problems of near-field microscopy that leverages the powerful framework of physics-…