A recent Nature study shows that separated artificial neural networks can accurately model SiC MOSFETs using minimal training data. Silicon carbide MOSFETs are increasingly replacing traditional ...

Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back Propagation in Neural Networks. In this video, we are using using binary ...

Neural networks aren’t the only game in artificial intelligence, but you’d be forgiven for thinking otherwise after the hot streak sparked by ChatGPT’s arrival in 2022. That model’s abilities, ...

Modern neural network architectures (e.g., transformers, but also simple feed-forward networks) can be equipped with a learnable embedding for categorical features. This should be mentioned on the ...

Neural networks power today’s AI boom. To understand them, all we need is a map, a cat and a few thousand dimensions. Look at a picture of a cat, and you’ll instantly recognize it as a cat. But try to ...

The neural networks dominating AI in recent years have achieved a remarkable level of behavioral flexibility, in part due to their capacity to learn new tasks from only a few examples. These ...

Abstract: The evaluation of teaching quality in higher mathematics courses presents significant challenges due to subjective assessment methods, varying instructional techniques, and diverse student ...

Abstract: Why quaternions in neural networks (NNs)? Are there quaternions in the human brain? “No” may be an ordinary answer. However, quaternion NNs (QNNs) are a powerful framework that strongly ...

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and ...