Pythonic What is Keras and how does it simplify neural network development

Sep 10, 2019 ... Using a genetic algorithm approach to find optimal neural network structures for model-free policy learning in reinforcement learning; Using ... Apr 8, 2025 ... Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data. By using algorithms, ... Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... We propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma ... The informational aspects of neural networks performing associative memory following Hebbian learning are analysed in detail. The recall process is decomposed ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ... Feb 26, 2024 ... This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more ... Neural networks, decision trees, simulation and ... The machine learning approach and the Information Virtue – Information Token – Informational. classification via convolutional deep neural network. In Proceedings of COLING. 2014, the 25th International Conference on Computational Linguistics ... Aug 30, 2023 ... To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to ...