A group of researchers has done a study with a network science approach, measuring the connections between different brain regions as participants learned to play a simple game. The differences in ...

If you use a 'constant' learning rate type, you specify that rate using the learning_rate_init parameter. This value often has a huge effect on the performance of the resulting neural network model.

Most of the challenge of performing neural network quantile regression is finding good values for the four key hyperparameters: number hidden nodes, learning rate, quantile rate, max epochs.

Artificial Neural Networks: Learning by Doing Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

Here, we feed the neural network vast amounts of training data, labeled by humans so that a neural network can essentially fact-check itself as it’s learning.

Can a neural network be constructed entirely from DNA and yet learn in the same way as its silicon-based brethren? Recent ...

Engineers have uncovered an unexpected pattern in how neural networks -- the systems leading today's AI revolution -- learn, suggesting an answer to one of the most important unanswered ...

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.