This paper proposes a novel end-to-end deep neural network architecture and adopts Gumbel distribution as an activation function in neural networks for class imbalance problem in the application of ...

The differences between neural network binary classification and multinomial classification are surprisingly tricky. In this article I explain two different approaches to implement neural network ...

This paper proposes a multi-task deep neural network (MT-DNN) architecture to handle the multi-label learning problem, in which each label learning is defined as a binary classification task, i.e., a ...

MLPs are most ideal for projects involving tabular datasets, classification prediction problems, and regression prediction problems. Convolution Neural Network. Convolution neural network (CNN) model ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.

Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of ...

With the use of proper neural network architecture (number of layers, number of neurons, non-linear function, etc.) along with large enough data, a deep learning network can learn any mapping from ...