Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

“Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog ...

It is a mathematical method for training neural networks to recognize patterns in data. The history and development of the backpropagation algorithm, including the contributions of Paul Werbos, take ...

Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of ...

Researchers at Google have open-sourced EvoLved sIgn mOmeNtum (Lion), an optimization algorithm for training neural networks, which was discovered using an automated machine learning (AutoML ...

As the new training technique still involves some level of manual annotation, the researchers hope to go on to develop a fully automatic algorithm for annotating and training models.

Machine learning needs to improve adversarial robustness in deep neural networks for robotics without reducing their accuracy and safety.

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java.

Artificial neural networks process data in a manner similar to the human brain.

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...