This study presents valuable computational findings on the neural basis of learning new motor memories without interfering with previously learned behaviours using recurrent neural networks. The ...

Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

Several significant research studies related to Preventing Phishing Attacks for Cyber Threat Mitigation have been reviewed ...

Learn how the Adagrad optimization algorithm works and see how to implement it step by step in pure Python — perfect for beginners in machine learning! #Adagrad #MachineLearning #PythonCoding ...

Confused by neural networks? Break it down step-by-step as we walk through forward propagation using Python—perfect for beginners and curious coders alike!

Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm ...

Robotic arms are increasingly being utilized in agriculture, where agility and precise movement are essential for their effective implementation. To enhance the performance of these manipulators, many ...

The course covers a wide range of topics, including the theory and math underlying deep learning, artificial neural networks, convolutional neural networks, and recurrent neural networks. Besides, ...

This paper presents the implementation of a deep, recurrent neural network (RNN) designed for classification of compliance between the original magnetic signature of a ship and the one obtained from a ...

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...